XPRSprob Class

Inheritance Hierarchy

System.Object
   Optimizer.XPRSobject
     Optimizer.XPRSprob
       Optimizer.Objects.XpressProblem

Namespace: Optimizer
Assembly: xprsdn (in xprsdn.dll) Version: 44.01.01

Syntax

public class XPRSprob : XPRSobject, ICloneable

The XPRSprob type exposes the following members.

Constructors

	Name	Description
	XPRSprob()	Creates a new, empty problem. Note that instances of this class should be explicitly cleaned up by calling Dispose() so that native resources can be released. A good practice is to use instances of this class in a 'using' statement
	XPRSprob(String)	Initialize Xpress and create a new problem.
	XPRSprob(IntPtr, XPRSprob)	Creates a new, problem around a given problem pointer. Only useful when accessing a problem created from another C library. Problems created in this way are assumed to be managed by another library and native resources are not released when the problem is disposed.

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Properties

	Name	Description
	ActiveNodes	Number of outstanding nodes.
	AlgAfterCrossOver	The algorithm to be used for the final clean up step after the crossover.
	AlgAfterNetwork	The algorithm to be used for the clean up step after the network simplex solver.
	Algorithm	The algorithm the optimizer currently is running / was running just before completition.
	AlternativeRedCosts	Controls aggressiveness of searching for alternative reduced cost
	AttentionLevel	A measure between 0 and 1 for how numerically unstable the problem is. The attention level is based on a weighted combination of the number of basis condition numbers exceeding certain thresholds. It considers all nodes sampled by MIPKAPPAFREQ, with a setting of 1 being the most frequent sampling rate. The higher the attention level, the worse conditioned is the problem.
	AutoCutting	Should the Optimizer automatically decide whether to generate cutting planes at local nodes in the tree or not? If the CUTFREQ control is set, no automatic selection will be made and local cutting will be enabled.
	AutoPerturb	Simplex: This indicates whether automatic perturbation is performed. If this is set to 1, the problem will be perturbed whenever the simplex method encounters an excessive number of degenerate pivot steps, thus preventing the Optimizer being hindered by degeneracies.
	AutoScaling	Whether the Optimizer should automatically select between different scaling algorithms. If the SCALING control is set, no automatic scaling will be applied.
	AvailableMemory	The amount of heap memory detected by Xpress as free.
	BackgroundMaxThreads	Limit the number of threads to use in background jobs (for example in parallel to the root cut loop).
	BackgroundSelect	Select which tasks to run in background jobs (for example in parallel to the root cut loop).
	BackTrack	Branch and Bound: Specifies how to select the next node to work on when a full backtrack is performed.
	BacktrackTie	Branch and Bound: Specifies how to break ties when selecting the next node to work on when a full backtrack is performed. The options are the same as for the BACKTRACK control.
	BarAASize	Number of nonzeros in AA ^T.
	BarAlg	This control determines which barrier algorithm is used to solve the problem.
	BarCGap	Convergence criterion for the Newton barrier algorithm.
	BarCondA	Absolute condition measure calculated in the last iteration of the barrier algorithm.
	BarCondD	Condition measure calculated in the last iteration of the barrier algorithm.
	BarCores	If set to a positive integer it determines the number of physical CPU cores assumed to be present in the system by the barrier and hybrid gradient algorithms. If the value is set to the default value ( -1), Xpress will automatically detect the number of cores.
	BarCrash	Newton barrier and hybrid gradient: This determines the type of crash used for the crossover. During the crash procedure, an initial basis is determined which attempts to speed up the crossover. A good choice at this stage will significantly reduce the number of iterations required to crossover to an optimal solution. The possible values increase proportionally to their time-consumption.
	BarCrossover	Indicates whether or not the basis crossover phase has been entered.
	BarDenseCol	Number of dense columns found in the matrix.
	BarDualInf	Sum of the dual infeasibilities for the Newton barrier algorithm.
	BarDualObj	Dual objective value calculated by the Newton barrier algorithm.
	BarDualStop	Newton barrier and hybrid gradient: This is a convergence parameter, representing the tolerance for dual infeasibilities. If the difference between the constraints and their bounds in the dual problem falls below this tolerance in absolute value, optimization will stop and the current solution will be returned.
	BarFailIterLimit	Newton barrier: The maximum number of consecutive iterations that fail to improve the solution in the barrier algorithm.
	BarFreeScale	Defines how the barrier algorithm scales free variables.
	BarGapStop	Newton barrier and hybrid gradient: This is a convergence parameter, representing the tolerance for the relative duality gap. When the difference between the primal and dual objective function values falls below this tolerance, the Optimizer determines that the optimal solution has been found.
	BarGapTarget	Newton barrier: The target tolerance for the relative duality gap. The barrier algorithm will keep iterating until either BARGAPTARGET is satisfied or until no further improvements are possible. In the latter case, if BARGAPSTOP is satisfied, it will declare the problem optimal.
	BarhgExtrapolate	Extrapolation parameter for the hybrid gradient algorithm. Although theory suggests that a value of 1 is best, slightly smaller values perform better in general.
	BarhgMaxRestarts	The maximum number of restarts in the hybrid gradient algorithm. Restarts play the role of iterations in the hybrid gradient algorithm. A log line is printed at every restart, unless BAROUTPUT is set to 0.
	BarhgOps	Control options for the hybrid gradient algorithm. Bits 1, 2 and 3 control which norms of the coefficient matrix are used for solution normalization. The normalization factor is the maximum of the selected norms. By default, or if all three bits are set to 0, the infinity norm is used. The omega parameter referenced in bits 4, 5 and 6 is a measure of the relative magnitudes of the objective and the right-hand side.
	BarIndefLimit	Newton Barrier. This limits the number of consecutive indefinite barrier iterations that will be performed. The optimizer will try to minimize (resp. maximize) a QP problem even if the Q matrix is not positive (resp. negative) semi-definite. However, the optimizer may detect that the Q matrix is indefinite and this can result in the optimizer not converging. This control specifies how many indefinite iterations may occur before the optimizer stops and reports that the problem is indefinite. It is usual to specify a value greater than one, and only stop after a series of indefinite matrices, as the problem may be found to be indefinite incorrectly on a few iterations for numerical reasons.
	BarIter	Number of Newton barrier iterations.
	BarIterLimit	Newton barrier: The maximum number of iterations. While the simplex method usually performs a number of iterations which is proportional to the number of constraints (rows) in a problem, the barrier method standardly finds the optimal solution to a given accuracy after a number of iterations which is independent of the problem size. The penalty is rather that the time for each iteration increases with the size of the problem. BARITERLIMIT specifies the maximum number of iterations which will be carried out by the barrier.
	BarKernel	Newton barrier: Defines how centrality is weighted in the barrier algorithm.
	BarLargeBound	Threshold for the barrier to handle large bounds.
	BarLSize	Number of nonzeros in L resulting from the Cholesky factorization.
	BarNumStability	Control BARNUMSTABILITY.
	BarObjPerturb	Defines how the barrier perturbs the objective.
	BarObjScale	Defines how the barrier scales the objective.
	BarOrder	Newton barrier: This controls the Cholesky factorization in the Newton-Barrier.
	BarOrderThreads	If set to a positive integer it determines the number of concurrent threads for the sparse matrix ordering algorithm in the Newton-barrier method.
	BarOutput	Newton barrier and hybrid gradient: This specifies the level of solution output provided. Output is provided either after each iteration of the algorithm, or else can be turned off completely by this parameter.
	BarPerturb	Newton barrier: In numerically challenging cases it is often advantageous to apply perturbations on the KKT system to improve its numerical properties. BARPERTURB controlls how much perturbation is allowed during the barrier iterations. By default no perturbation is allowed. Set this parameter with care as larger perturbations may lead to less efficient iterates and the best settings are problem-dependent.
	BarPresolveOps	Newton barrier: This controls the Newton-Barrier specific presolve operations.
	BarPrimalInf	Sum of the primal infeasibilities for the Newton barrier algorithm.
	BarPrimalObj	Primal objective value calculated by the Newton barrier algorithm.
	BarPrimalStop	Newton barrier and hybrid gradient: This is a convergence parameter, indicating the tolerance for primal infeasibilities. If the difference between the constraints and their bounds in the primal problem falls below this tolerance in absolute value, the Optimizer will terminate and return the current solution.
	BarRefIter	Newton barrier: After terminating the barrier algorithm, further refinement steps can be performed. Such refinement steps are especially helpful if the solution is near to the optimum and can improve primal feasibility and decrease the complementarity gap. It is also often advantageous for the crossover algorithm. BARREFITER specifies the maximum number of such refinement iterations.
	BarRegularize	This control determines how the barrier algorithm applies regularization on the KKT system.
	BarRhsScale	Defines how the barrier scales the right hand side.
	BarSing	Number of linearly dependent binding constraints at the optimal barrier solution. These results in singularities in the Cholesky decomposition during the barrier that may cause numerical troubles. Larger dependence means more chance for numerical difficulties.
	BarSingR	Regularized number of linearly dependent binding constraints at the optimal barrier solution. These results in singularities in the Cholesky decomposition during the barrier that may cause numerical troubles. Larger dependence means more chance for numerical difficulties.
	BarSolution	This determines whether the barrier has to decide which is the best solution found or return the solution computed by the last barrier iteration.
	BarStart	Controls the computation of the starting point and warm-starting for the Newton barrier and the hybrid gradient algorithms.
	BarStartWeight	Newton barrier: This sets a weight for the warm-start point when warm-start is set for the barrier algorithm. Using larger weight gives more emphasis for the supplied starting point.
	BarStepStop	Newton barrier: A convergence parameter, representing the minimal step size. On each iteration of the barrier algorithm, a step is taken along a computed search direction. If that step size is smaller than BARSTEPSTOP, the Optimizer will terminate and return the current solution.
	BarThreads	If set to a positive integer it determines the number of threads implemented to run the Newton-barrier and hybrid gradient algorithms. If the value is set to the default value ( -1), the THREADS control will determine the number of threads used.
	BestBound	Value of the best bound determined so far by the MIP search.
	BigM	The infeasibility penalty used if the "Big M" method is implemented.
	BigmMethod	Simplex: This specifies whether to use the "Big M" method, or the standard phase I (achieving feasibility) and phase II (achieving optimality). In the "Big M" method, the objective coefficients of the variables are considered during the feasibility phase, possibly leading to an initial feasible basis which is closer to optimal. The side-effects involve possible round-off errors due to the presence of the "Big M" factor in the problem.
	BoundName	Active bound name.
	BranchChoice	Once a MIP entity has been selected for branching, this control determines which of the branches is solved first.
	BranchDisj	Branch and Bound: Determines whether the optimizer should attempt to branch on general split disjunctions during the branch and bound search.
	BranchStructural	Branch and Bound: Determines whether the optimizer should search for special structure in the problem to branch on during the branch and bound search.
	BranchValue	The value of the branching variable at a node of the Branch and Bound tree.
	BranchVar	The branching variable at a node of the Branch and Bound tree.
	BreadthFirst	The number of nodes to include in the best-first search before switching to the local first search ( NODESELECTION = 4).
	CacheSize	Obsolete. Newton Barrier: L2 or L3 (see notes) cache size in kB (kilobytes) of the CPU. On Intel (or compatible) platforms a value of -1 may be used to determine the cache size automatically. If the CPU model is new then the cache size may not be correctly detected by an older release of the software.
	CallbackCheckTimeDelay	Minimum delay in milliseconds between two consecutive executions of the CHECKTIME callback in the same solution process
	CallbackCount_CutMgr	This attribute counts the number of times the cut manager callback set by addCbCutmgr has been called for the current node, including the current callback call. The value of this attribute should only be used from within the cut manager callback.
	CallbackCount_OptNode	This attribute counts the number of times the optimal node callback set by addCbOptnode has been called for the current node, including the current callback call. The value of this attribute should only be used from within the optimal node callback.
	CallbackFromMasterThread	Branch and Bound: specifies whether the MIP callbacks should only be called on the master thread.
	CheckInputData	Check input arrays for bad data.
	ChecksOnMaxCutTime	This attribute is used to set the value of the MAXCHECKSONMAXCUTTIME control. Its value is the number of times the optimizer checked the MAXCUTTIME criterion during the last call to the optimization routine mipOptimize. If a run terminates cutting operations on the MAXCUTTIME criterion then the attribute is the negative of the number of times the optimizer checked the MAXCUTTIME criterion up to and including the check when the termination was activated. Note that the attribute is set to zero at the beginning of each call to an optimization routine.
	ChecksOnMaxTime	This attribute is used to set the value of the MAXCHECKSONMAXTIME control. Its value is the number of times the optimizer checked the MAXTIME criterion during the last call to the optimization routine mipOptimize. If a run terminates on the MAXTIME criterion then the attribute is the negative of the number of times the optimizer checked the MAXTIME criterion up to and including the check when the termination was activated. Note that the attribute is set to zero at the beginning of each call to an optimization routine.
	CholeskyAlg	Newton barrier: type of Cholesky factorization used.
	CholeskyTol	Newton barrier: The tolerance for pivot elements in the Cholesky decomposition of the normal equations coefficient matrix, computed at each iteration of the barrier algorithm. If the absolute value of the pivot element is less than or equal to CHOLESKYTOL, it merits special treatment in the Cholesky decomposition process.
	Clamping	This control allows for the adjustment of returned solution values such that they are always within bounds.
	Cols	Number of columns (i.e. variables) in the matrix.
	Compute	Controls whether the next solve is performed directly or on an Insight Compute Interface.
	ComputeExecService	Selects the Insight execution service that will be used for solving remote optimizations.
	ComputeExecutions	The number of solves executed on a compute server.
	ComputeJobPriority	Selects the priority that will be used for remote optimization jobs.
	ComputeLog	Controls how the run log is fetched when a solve is performed on an Insight Compute Interface.
	ConcurrentThreads	Determines the number of threads used by the concurrent solver.
	ConeElems	Number of second order cone coefficients in the problem.
	Cones	Number of second order and rotated second order cones in the problem.
	ConflictCuts	Branch and Bound: Specifies how cautious or aggressive the optimizer should be when searching for and applying conflict cuts. Conflict cuts are in-tree cuts derived from nodes found to be infeasible or cut off, which can be used to cut off other branches of the search tree.
	CoresDetected	Number of logical cores detected by the optimizer, which is the total number of threads the hardware can execute across all CPUs.
	CoresPerCPU	Used to override the detected value of the number of cores on a CPU. The cache size (either detected or specified via the CACHESIZE control) used in Barrier methods will be divided by this amount, and this scaled-down value will be the amount of cache allocated to each Barrier thread
	CoresPerCPUDetected	Number of logical cores per CPU unit detected by the optimizer, which is the number of threads each CPU can execute.
	CoverCuts	Branch and Bound: The number of rounds of lifted cover inequalities at the top node. A lifted cover inequality is an additional constraint that can be particularly effective at reducing the size of the feasible region without removing potential integral solutions. The process of generating these can be carried out a number of times, further reducing the feasible region, albeit incurring a time penalty. There is usually a good payoff from generating these at the top node, since these inequalities then apply to every subsequent node in the tree search.
	CpiAlpha	decay term for confined primal integral computation.
	CpiScaleFactor	scale factor from primal integral computation.
	CPUPlatform	Newton Barrier: Selects the AMD, Intel x86 or ARM vectorization instruction set that Barrier should run optimized code for. On AMD / Intel x86 platforms the SSE2, AVX and AVX2 instruction sets are supported while on ARM platforms the NEON architecture extension can be activated.
	CPUsDetected	Number of CPU units detected by the optimizer.
	CPUTime	How time should be measured when timings are reported in the log and when checking against time limits
	Crash	Simplex: This determines the type of crash used when the algorithm begins. During the crash procedure, an initial basis is determined which is as close to feasibility and triangularity as possible. A good choice at this stage will significantly reduce the number of iterations required to find an optimal solution. The possible values increase proportionally to their time-consumption.
	CrossOver	Newton barrier and hybrid gradient: This control determines whether the barrier method will cross over to the simplex method when at optimal solution has been found, to provide an end basis (see getBasis, writeBasis) and advanced sensitivity analysis information (see objsa, rHSsa, bndsa).
	CrossoverAccuracyTol	Newton barrier: This control determines how crossover adjusts the default relative pivot tolerance. When re-inversion is necessary, crossover will compare the recalculated working basic solution with the assumed ones just before re-inversion took place. If the error is above this threshold, crossover will adjust the relative pivot tolerance to address the build-up of numerical inaccuracies.
	CrossOverDRP	Control CROSSOVERDRP.
	CrossOverFeasWeight	Control CROSSOVERFEASWEIGHT.
	CrossoverIter	Number of simplex iterations performed in crossover.
	CrossoverIterLimit	Newton barrier and hybrid gradient: The maximum number of iterations that will be performed in the crossover procedure before the optimization process terminates.
	CrossoverOps	Newton barrier and hybrid gradient: a bit vector for adjusting the behavior of the crossover procedure.
	CrossOverRelPivotTol	Control CROSSOVERRELPIVOTTOL.
	CrossOverRelPivotTolSafe	Control CROSSOVERRELPIVOTTOLSAFE.
	CrossoverThreads	Determines the maximum number of threads that parallel crossover is allowed to use. If CROSSOVERTHREADS is set to the default value ( -1), the BARTHREADS control will determine the number of threads used.
	CurrentMemory	The amount of dynamically allocated heap memory by the problem being solved.
	CurrentNode	The unique identifier of the current node in the tree search.
	CurrMipCutOff	The current MIP cut off.
	CutDepth	Branch and Bound: Sets the maximum depth in the tree search at which cuts will be generated. Generating cuts can take a lot of time, and is often less important at deeper levels of the tree since tighter bounds on the variables have already reduced the feasible region. A value of 0 signifies that no cuts will be generated.
	CutFactor	Limit on the number of cuts and cut coefficients the optimizer is allowed to add to the matrix during tree search. The cuts and cut coefficients are limited by CUTFACTOR times the number of rows and coefficients in the initial matrix.
	CutFreq	Branch and Bound: This specifies the frequency at which cuts are generated in the tree search. If the depth of the node modulo CUTFREQ is zero, then cuts will be generated.
	Cuts	Number of cuts being added to the matrix.
	CutSelect	A bit vector providing detailed control of the cuts created for the root node of a MIP solve. Use TREECUTSELECT to control cuts during the tree search.
	CutStrategy	Branch and Bound: This specifies the cut strategy. A more aggressive cut strategy, generating a greater number of cuts, will result in fewer nodes to be explored, but with an associated time cost in generating the cuts. The fewer cuts generated, the less time taken, but the greater subsequent number of nodes to be explored.
	DefaultAlg	This selects the algorithm that will be used to solve LPs, standalone or during MIP optimization.
	DenseColLimit	Newton barrier: Columns with more than DENSECOLLIMIT elements are considered to be dense. Such columns will be handled specially in the Cholesky factorization of this matrix.
	Deterministic	Selects whether to use a deterministic or opportunistic mode when solving a problem using multiple threads.
	DetLogFreq	Control DETLOGFREQ.
	DualGradient	Simplex: This specifies the dual simplex pricing method.
	DualInfeas	Number of dual infeasibilities.
	Dualize	For a linear problem or the initial linear relaxation of a MIP, determines whether to form and solve the dual problem.
	DualizeOps	Bit-vector control for adjusting the behavior when a problem is dualized.
	DualPerturb	The factor by which the problem will be perturbed prior to optimization by dual simplex. A value of 0.0 results in no perturbation prior to optimization. Note the interconnection to the AUTOPERTURB control. If AUTOPERTURB is set to 1, the decision whether to perturb or not is left to the Optimizer. When the problem is automatically perturbed in dual simplex, however, the value of DUALPERTURB will be used for perturbation.
	DualStrategy	This bit-vector control specifies the dual simplex strategy.
	DualThreads	Determines the maximum number of threads that dual simplex is allowed to use. If DUALTHREADS is set to the default value ( -1), the THREADS control will determine the number of threads used.
	DummyControl	Control DUMMYCONTROL.
	EigenValueTol	A quadratic matrix is considered not to be positive semi-definite, if its smallest eigenvalue is smaller than the negative of this value.
	Elems	Number of matrix nonzeros (elements).
	ElimFillIn	Amount of fill-in allowed when performing an elimination in presolve .
	ElimTol	The Markowitz tolerance for the elimination phase of the presolve.
	ErrorCode	The most recent Optimizer error number that occurred. This is useful to determine the precise error or warning that has occurred, after an Optimizer function has signalled an error by returning a non-zero value. The return value itself is not the error number. Refer to the section for a list of possible error numbers, the errors and warnings that they indicate, and advice on what they mean and how to resolve them. A short error message may be obtained using getLastError, and all messages may be intercepted using the user output callback function; see addCbMessage.
	EscapeNames	If characters illegal to an mps or lp file should be escaped to guarantee readability, and whether escaped characters should be transformed back when reading such a file.
	EtaTol	Tolerance on eta elements. During each iteration, the basis inverse is premultiplied by an elementary matrix, which is the identity except for one column - the eta vector. Elements of eta vectors whose absolute value is smaller than ETATOL are taken to be zero in this step.
	ExtraCols	The initial number of extra columns to allow for in the matrix. If columns are to be added to the matrix, then, for maximum efficiency, space should be reserved for the columns before the matrix is input by setting the EXTRACOLS control. If this is not done, resizing will occur automatically, but more space may be allocated than the user actually requires.
	ExtraElems	The initial number of extra matrix elements to allow for in the matrix, including coefficients for cuts. If rows or columns are to be added to the matrix, then, for maximum efficiency, space should be reserved for the extra matrix elements before the matrix is input by setting the EXTRAELEMS control. If this is not done, resizing will occur automatically, but more space may be allocated than the user actually requires.
	ExtraMIPEnts	The initial number of extra MIP entities to allow for.
	ExtraRows	The initial number of extra rows to allow for in the matrix, including cuts. If rows are to be added to the matrix, then, for maximum efficiency, space should be reserved for the rows before the matrix is input by setting the EXTRAROWS control. If this is not done, resizing will occur automatically, but more space may be allocated than the user actually requires.
	ExtraSetElems	The initial number of extra elements in sets to allow for in the matrix. If sets are to be added to the matrix, then, for maximum efficiency, space should be reserved for the set elements before the matrix is input by setting the EXTRASETELEMS control. If this is not done, resizing will occur automatically, but more space may be allocated than the user actually requires.
	ExtraSets	The initial number of extra sets to allow for in the matrix. If sets are to be added to the matrix, then, for maximum efficiency, space should be reserved for the sets before the matrix is input by setting the EXTRASETS control. If this is not done, resizing will occur automatically, but more space may be allocated than the user actually requires.
	FeasibilityJump	MIP: Decides if the Feasibility Jump heuristic should be run. The value for this control is either -1 (let Xpress decide), 0 (off) or a value that indicates for which type of models the heuristic should be run.
	FeasibilityPump	Branch and Bound: Decides if the Feasibility Pump heuristic should be run at the top node.
	FeasTol	This tolerance determines when a solution is treated as feasible. If the amount by which a constraint's activity violates its right-hand side or ranged bound is less in absolute magnitude than FEASTOL, then the constraint is treated as satisfied. Similarly, if the amount by which a column violates its bounds is less in absolute magnitude than FEASTOL, those bounds are also treated as satisfied.
	FeasTolPerturb	This tolerance determines how much a feasible primal basic solution is allowed to be perturbed when performing basis changes. The tolerance FEASTOL is always considered as an upper limit for the perturbations, but in some cases smaller value can be more desirable.
	FeasTolTarget	This specifies the target feasibility tolerance for the solution refiner.
	ForceOutput	Certain names in the problem object may be incompatible with different file formats (such as names containing spaces for LP files). If the optimizer might be unable to read back a problem because of non-standard names, it will first attempt to write it out using an extended naming convention. If the names would not be possible to extend so that they would be reproducible and recognizable, it will give an error message and won't create the file. If the optimizer might be unable to read back a problem because of non-standard names, it will give an error message and won't create the file. This option may be used to force output anyway.
	ForceParallelDual	Dual simplex: specifies whether the dual simplex solver should always use the parallel simplex algorithm. By default, when using a single thread, the dual simplex solver will execute a dedicated sequential simplex algorithm.
	GenConCols	Number of input variables in general constraints (i.e. MIN/MAX/AND/OR/ABS constraints) in the problem.
	GenCons	The number of general constraints (i.e. MIN/MAX/AND/OR/ABS constraints) in the problem.
	GenconsAbsTransformation	This control specifies the reformulation method for absolute value general constraints at the beginning of the search.
	GenconsDualReductions	This parameter specifies whether dual reductions should be applied to reduce the number of columns and rows added when transforming general constraints to MIP structs.
	GenConVals	Number of constant values in general constraints (MIN/MAX constraints) in the problem.
	GlobalBoundingBox	If a nonlinear problem cannot be solved due to appearing unbounded, it can automatically be regularized by the application of a bounding box on the variables. If this control is set to a negative value, in a second solving attempt all original variables will be bounded by the absolute value of this control. If set to a positive value, there will be a third solving attempt afterwards, if necessary, in which also all auxiliary variables are bounded by this value.
	GlobalBoundingboxApplied	Whether a bounding box equal to the absolute value of the GLOBALBOUNDINGBOX control was applied to the problem after the initial solve came back infeasible and if so, to which variables.
	GlobalLSHeurstrategy	When integer-feasible (for MINLP, any solution for NLP) but nonlinear-infeasible solutions are encountered within a global solve, the integer variables can be fixed and a local solver (as defined by the LOCALSOLVER control) can be called on the remaining continuous problem. This control defines the frequency and effort of such local solves.
	GlobalNlpCuts	Limit on the number of rounds of outer approximation and convexification cuts generated for the root node, when solving an (MI)NLP to global optimality.
	GlobalNLPInfeas	Number of nonlinear infeasibilities at the current node of a global solve, measured as the number of violated atomic formulas.
	GlobalNumInitNlpCuts	Specifies the maximum number of tangent cuts when setting up the initial relaxation during a global solve. By default, the algorithm chooses the number of cuts automatically. Adding more cuts tightens the problem, resulting in a smaller branch-and-bound tree, at the cost of slowing down each LP solve.
	GlobalSpatialBranchCuttingEffort	Limits the effort that is spent on creating cuts during spatial branching.
	GlobalSpatialBranchIfPreferOrig	Whether spatial branchings on original variables should be preferred over branching on auxiliary variables that were introduced by the reformulation of the global solver.
	GlobalSpatialBranchPropagationEffort	Limits the effort that is spent on propagation during spatial branching.
	GlobalTreeNlpCuts	Limit on the number of rounds of outer approximation and convexification cuts generated for each node in the tree, when solving an (MI)NLP to global optimality.
	GomCuts	Branch and Bound: The number of rounds of Gomory or lift-and-project cuts at the top node.
	HeurBeforeLP	Branch and Bound: Determines whether primal heuristics should be run before the initial LP relaxation has been solved.
	HeurDepth	Branch and Bound: Sets the maximum depth in the tree search at which heuristics will be used to find MIP solutions. It may be worth stopping the heuristic search for solutions after a certain depth in the tree search. A value of 0 signifies that heuristics will not be used. This control no longer has any effect and will be removed from future releases.
	HeurDiveIterLimit	Branch and Bound: Simplex iteration limit for reoptimizing during the diving heuristic.
	HeurDiveRandomize	The level of randomization to apply in the diving heuristic. The diving heuristic uses priority weights on rows and columns to determine the order in which to e.g. round fractional columns, or the direction in which to round them. This control determines by how large a random factor these weights should be changed.
	HeurDiveSoftRounding	Branch and Bound: Enables a more cautious strategy for the diving heuristic, where it tries to push binaries and integer variables to their bounds using the objective, instead of directly fixing them. This can be useful when the default diving heuristics fail to find any feasible solutions.
	HeurDiveSpeedUp	Branch and Bound: Changes the emphasis of the diving heuristic from solution quality to diving speed.
	HeurDiveStrategy	Branch and Bound: Chooses the strategy for the diving heuristic.
	HeurEmphasis	Branch and Bound: This control specifies an emphasis for the search w.r.t. primal heuristics and other procedures that affect the speed of convergence of the primal-dual gap. For problems where the goal is to achieve a small gap but not neccessarily solving them to optimality, it is recommended to set HEUREMPHASIS to 1. This setting triggers many additional heuristic calls, aiming for reducing the gap at the beginning of the search, typically at the expense of an increased time for proving optimality.
	HeurForceSpecialObj	Branch and Bound: This specifies whether local search heuristics without objective or with an auxiliary objective should always be used, despite the automatic selection of the Optimiezr. Deactivated by default.
	HeurFreq	Branch and Bound: This specifies the frequency at which heuristics are used in the tree search. Heuristics will only be used at a node if the depth of the node is a multiple of HEURFREQ.
	HeurMaxSol	Branch and Bound: This specifies the maximum number of heuristic solutions that will be found in the tree search. This control no longer has any effect and will be removed from future releases.
	HeurNodes	Branch and Bound: This specifies the maximum number of nodes at which heuristics are used in the tree search. This control no longer has any effect and will be removed from future releases.
	HeursearchBackgroundSelect	Select which large neighborhood searches to run in the background (for example in parallel to the root cut loop).
	HeurSearchCopyControls	Select how user-set controls should affect local search heuristics.
	HeurSearchEffort	Adjusts the overall level of the local search heuristics.
	HeurSearchFreq	Branch and Bound: This specifies how often the local search heuristic should be run in the tree.
	HeurSearchRootCutFreq	How frequently to run the local search heuristic during root cutting. This is given as how many cut rounds to perform between runs of the heuristic. Set to zero to avoid applying the heuristic during root cutting. Branch and Bound: This specifies how often the local search heuristic should be run in the tree.
	HeurSearchRootSelect	A bit vector control for selecting which local search heuristics to apply on the root node of a MIP solve. Use HEURSEARCHTREESELECT to control local search heuristics during the tree search.
	HeurSearchTargetSize	Control HEURSEARCHTARGETSIZE.
	HeurSearchTreeSelect	A bit vector control for selecting which local search heuristics to apply during the tree search of a MIP solve. Use HEURSEARCHROOTSELECT to control local search heuristics on the root node.
	HeurSelect	Control HEURSELECT.
	HeurShiftProp	Determines whether the Shift-and-propagate primal heuristic should be executed. If enabled, Shift-and-propagate is an LP-free primal heuristic that is executed immediately after presolve.
	HeurThreads	Branch and Bound: The number of threads to dedicate to running heuristics during the root solve.
	HistoryCosts	Branch and Bound: How to update the pseudo cost for a MIP entity when a strong branch or a regular branch is applied.
	IfCheckConvexity	Determines if the convexity of the problem is checked before optimization. Applies to quadratic, mixed integer quadratic and quadratically constrained problems. Checking convexity takes some time, thus for problems that are known to be convex it might be reasonable to switch the checking off.
	IgnoreContainerCpuLimit	Control IGNORECONTAINERCPULIMIT.
	IgnoreContainerMemoryLimit	Control IGNORECONTAINERMEMORYLIMIT.
	IISLog	Selects how much information should be printed during the IIS procedure. Please refer to Appendix for a more detailed description of the IIS logging format.
	IISOps	Selects which part of the restrictions (bounds, constraints, entities) should always be kept in an IIS. This is useful if certain types of restrictions cannot be violated, thus they are known not to be the cause of infeasibility. The IIS obtained this way is irreducible only for the non-protected restrictions. This control has an effect only on the deletion filter of the IIS procedure.
	IISSolStatus	IIS solution status.
	Indicators	Number of indicator constrains in the problem.
	IndLinBigM	During presolve, indicator constraints will be linearized using a BigM coefficient whenever that BigM coefficient is small enough. This control defines the largest BigM for which such a linearized version will be added to the problem in addition to the original constraint. If the BigM is even smaller than INDPRELINBIGM, then the original indicator constraint will additionally be dropped from the problem.
	IndPreLinBigM	During presolve, indicator constraints will be linearized using a BigM coefficient whenever that BigM coefficient is small enough. This control defines the largest BigM for which the original constraint will be replaced by the linearized version. If the BigM is larger than INDPRELINBIGM but smaller than INDLINBIGM, the linearized row will be added but the original indicator constraint is kept as a numerically stable way to check feasibility.
	InputCols	Number of columns (i.e. variables) in the original matrix before nonlinear reformulations.
	InputRows	Number of rows (i.e. constraints) in the original matrix before nonlinear reformulations.
	Inputtol	The tolerance on input values elements. If any value is less than or equal to INPUTTOL in absolute value, it is treated as zero. For the internal zero tolerance see MATRIXTOL.
	InvertFreq	Simplex: The frequency with which the basis will be inverted. The basis is maintained in a factorized form and on most simplex iterations it is incrementally updated to reflect the step just taken. This is considerably faster than computing the full inverted matrix at each iteration, although after a number of iterations the basis becomes less well-conditioned and it becomes necessary to compute the full inverted matrix. The value of INVERTFREQ specifies the maximum number of iterations between full inversions.
	InvertMin	Simplex: The minimum number of iterations between full inversions of the basis matrix. See the description of INVERTFREQ for details.
	IOTimeout	The maximum number of seconds to wait for an I/O operation before it is cancelled.
	KeepBasis	Simplex: This determines whether the basis should be kept when reoptimizing a problem. The choice is between using a crash basis created at the beginning of simplex or using a basis from a previous solve (if such exists). By default, this control gets (re)set automatically in various situations. By default, it will be automatically set to 1 after a solve that produced a valid basis. This will automatically warmstart a subsequent solve. Explicitly loading a starting basis will also set this control to 1. If the control is explicitly set to 0, any existing basis will be ignored for a new solve, and the Optimizer will start from an ad-hoc crash basis.
	KeepNRows	How nonbinding rows should be handled by the MPS reader.
	KnitroParamAlgorithm	Indicates which algorithm to use to solve the problem
	KnitroParamBarDirectInterval	Controls the maximum number of consecutive conjugate gradient (CG) steps before Knitro will try to enforce that a step is taken using direct linear algebra.
	KnitroParamBarFeasible	Specifies whether special emphasis is placed on getting and staying feasible in the interior-point algorithms.
	KnitroParamBarFeasModeTol	Specifies the tolerance in equation that determines whether Knitro will force subsequent iterates to remain feasible.
	KnitroParamBarInitMu	Specifies the initial value for the barrier parameter : mu used with the barrier algorithms. This option has no effect on the Active Set algorithm.
	KnitroParamBarInitPt	Indicates whether an initial point strategy is used with barrier algorithms.
	KnitroParamBarMaxBacktrack	Indicates the maximum allowable number of backtracks during the linesearch of the Interior/Direct algorithm before reverting to a CG step.
	KnitroParamBarMaxRefactor	Indicates the maximum number of refactorizations of the KKT system per iteration of the Interior/Direct algorithm before reverting to a CG step.
	KnitroParamBarMuRule	Indicates which strategy to use for modifying the barrier parameter mu in the barrier algorithms.
	KnitroParamBarPenCons	Indicates whether a penalty approach is applied to the constraints.
	KnitroParamBarPenRule	Indicates which penalty parameter strategy to use for determining whether or not to accept a trial iterate.
	KnitroParamBarRelaxCons
	KnitroParamBarSwitchRule	Indicates whether or not the barrier algorithms will allow switching from an optimality phase to a pure feasibility phase.
	KnitroParamBLASOption
	KnitroParamDebug
	KnitroParamDelta	Specifies the initial trust region radius scaling factor used to determine the initial trust region size.
	KnitroParamFeastol	Specifies the final relative stopping tolerance for the feasibility error.
	KnitroParamFeasTolAbs	Specifies the final absolute stopping tolerance for the feasibility error.
	KnitroParamGradOpt	Specifies how to compute the gradients of the objective and constraint functions.
	KnitroParamHessOpt	Specifies how to compute the (approximate) Hessian of the Lagrangian.
	KnitroParamHonorBbnds	Indicates whether or not to enforce satisfaction of simple variable bounds throughout the optimization.
	KnitroParamInfeasTol	Specifies the (relative) tolerance used for declaring infeasibility of a model.
	KnitroParamLinSolver
	KnitroParamLMSize	Specifies the number of limited memory pairs stored when approximating the Hessian using the limited-memory quasi-Newton BFGS option.
	KnitroParamMATerminate
	KnitroParamMaxCGIt	Specifies the number of limited memory pairs stored when approximating the Hessian using the limited-memory quasi-Newton BFGS option.
	KnitroParamMaxCrossIt	Specifies the maximum number of crossover iterations before termination.
	KnitroParamMaxIt	Specifies the maximum number of iterations before termination.
	KnitroParamMIPBranchRule	Specifies which branching rule to use for MIP branch and bound procedure.
	KnitroParamMIPDebug
	KnitroParamMIPGUBBranch	Specifies whether or not to branch on generalized upper bounds (GUBs).
	KnitroParamMIPHeuristic	Specifies which MIP heuristic search approach to apply to try to find an initial integer feasible point.
	KnitroParamMIPHeurMaxIt
	KnitroParamMIPImplicatns	Specifies whether or not to add constraints to the MIP derived from logical implications.
	KnitroParamMIPIntGapAbs	The absolute integrality gap stop tolerance for MIP.
	KnitroParamMIPIntGapRel	The relative integrality gap stop tolerance for MIP.
	KnitroParamMIPKnapsack	Specifies rules for adding MIP knapsack cuts.
	KnitroParamMIPLPAlg	Specifies which algorithm to use for any linear programming (LP) subproblem solves that may occur in the MIP branch and bound procedure.
	KnitroParamMIPMaxNodes	Specifies the maximum number of nodes explored.
	KnitroParamMIPMethod	Specifies which MIP method to use.
	KnitroParamMIPOutInterval	Specifies node printing interval for XKTR_PARAM_MIP_OUTLEVEL when XKTR_PARAM_MIP_OUTLEVEL > 0.
	KnitroParamMIPOutLevel	Specifies how much MIP information to print.
	KnitroParamMIPPseudoint	Specifies the method used to initialize pseudo-costs corresponding to variables that have not yet been branched on in the MIP method.
	KnitroParamMIPRootAlg	Specifies which algorithm to use for the root node solve in MIP (same options as XKTR_PARAM_ALGORITHM user option).
	KnitroParamMIPRounding	Specifies the MIP rounding rule to apply.
	KnitroParamMIPSelectRule	Specifies the MIP select rule for choosing the next node in the branch and bound tree.
	KnitroParamMIPStringMaxIt	Specifies the maximum number of iterations to allow for MIP strong branching solves.
	KnitroParamMIPStrongCandLim	Specifies the maximum number of candidates to explore for MIP strong branching.
	KnitroParamMIPStrongLevel	Specifies the maximum number of tree levels on which to perform MIP strong branching.
	KnitroParamMsMaxBndRange
	KnitroParamMSMaxSolves
	KnitroParamMSNumToSave
	KnitroParamMSSaveTol
	KnitroParamMSSeed
	KnitroParamMSStartPtRange
	KnitroParamMSTerminate
	KnitroParamMultiStart
	KnitroParamNewPoint
	KnitroParamObjRange	Specifies the extreme limits of the objective function for purposes of determining unboundedness.
	KnitroParamOptTol	Specifies the final relative stopping tolerance for the KKT (optimality) error.
	KnitroParamOptTolAbs	Specifies the final absolute stopping tolerance for the KKT (optimality) error.
	KnitroParamOutLev	Controls the level of output produced by Knitro.
	KnitroParamParNumThreads
	KnitroParamPivot
	KnitroParamPresolve	Determine whether or not to use the Knitro presolver to try to simplify the model by removing variables or constraints. Specifies conditions for terminating the MIP algorithm.
	KnitroParamPresolveTol	Determines the tolerance used by the Knitro presolver to remove variables and constraints from the model.
	KnitroParamScale	Performs a scaling of the objective and constraint functions based on their values at the initial point.
	KnitroParamSOC	Specifies whether or not to try second order corrections (SOC).
	KnitroParamXTol	The optimization process will terminate if the relative change in all components of the solution point estimate is less than xtol.
	L1Cache	Obsolete. Newton barrier: L1 cache size in kB (kilo bytes) of the CPU. On Intel (or compatible) platforms a value of -1 may be used to determine the cache size automatically.
	LNPBest	Number of infeasible MIP entities to create lift-and-project cuts for during each round of Gomory cuts at the top node (see GOMCUTS).
	LNPIterLimit	Number of iterations to perform in improving each lift-and-project cut.
	LocalBacktrack	Control LOCALBACKTRACK.
	LocalChoice	Controls when to perform a local backtrack between the two child nodes during a dive in the branch and bound tree.
	LocalSolver
	LocalSolverSelected
	LPFlags	A bit-vector control which defines the algorithm for solving an LP problem or the initial LP relaxation of a MIP problem.
	LpFolding	Simplex and barrier: whether to fold an LP problem before solving it.
	LPIterLimit	The maximum number of iterations that will be performed by primal simplex or dual simplex before the optimization process terminates. For MIP problems, this is the maximum total number of iterations over all nodes explored by the Branch and Bound method.
	LPLog	Simplex: The frequency at which the simplex log is printed.
	LPLogDelay	Time interval between two LP log lines.
	LPLogStyle	Simplex: The style of the simplex log.
	LPObjVal	Value of the objective function of the last LP solved.
	LpRefineIterLimit	This specifies the simplex iteration limit the solution refiner can spend in attempting to increase the accuracy of an LP solution.
	LPStatus	LP solution status.
	LUPivotTol	Control LUPIVOTTOL.
	MarkowitzTol	The Markowitz tolerance used for the factorization of the basis matrix.
	MatrixName	The matrix name.
	MatrixTol	The zero tolerance on matrix elements. If the value of a matrix element is less than or equal to MATRIXTOL in absolute value, it is treated as zero. The control applies when solving a problem, for an input tolerance see INPUTTOL.
	MaxAbsDualInfeas	Maximum calculated absolute dual infeasibility in the unscaled original problem.
	MaxAbsPrimalInfeas	Maximum calculated absolute primal infeasibility in the unscaled original problem.
	MaxChecksOnMaxCutTime	This control is intended for use where optimization runs that are terminated using the MAXCUTTIME control are required to be reproduced exactly. This control is necessary because of the inherent difficulty in terminating algorithmic software in a consistent way using temporal criteria. The control value relates to the number of times the optimizer checks the MAXCUTTIME criterion up to and including the check when the termination of cutting was activated. To use the control the user first must obtain the value of the CHECKSONMAXCUTTIME attribute after the run returns. This attribute value is the number of times the optimizer checked the MAXCUTTIME criterion during the last call to the optimization routine mipOptimize. Note that this attribute value will be negative if the optimizer terminated cutting on the MAXCUTTIME criterion. To ensure accurate reproduction of a run the user should first ensure that MAXCUTTIME is set to its default value or to a large value so the run does not terminate again on MAXCUTTIME and then simply set the control MAXCHECKSONMAXCUTTIME to the absolute value of the CHECKSONMAXCUTTIME value.
	MaxChecksOnMaxTime	This control is intended for use where optimization runs that are terminated using the TIMELIMIT (or the deprecated MAXTIME) control are required to be reproduced exactly. This control is necessary because of the inherent difficulty in terminating algorithmic software in a consistent way using temporal criteria. The control value relates to the number of times the optimizer checks the TIMELIMIT criterion up to and including the check when the termination was activated. To use the control the user first must obtain the value of the CHECKSONMAXTIME attribute after the run returns. This attribute value is the number of times the optimizer checked the TIMELIMIT criterion during the last call to the optimization routine mipOptimize. Note that this attribute value will be negative if the optimizer terminated on the TIMELIMIT criterion. To ensure that a reproduction of a run terminates in the same way the user should first ensure that TIMELIMIT is set to its default value or to a large value so the run does not terminate again on TIMELIMIT and then simply set the control MAXCHECKSONMAXTIME to the absolute value of the CHECKSONMAXTIME value.
	MaxCutTime	The maximum amount of time allowed for generation of cutting planes and reoptimization. The limit is checked during generation and no further cuts are added once this limit has been exceeded.
	MaxIIS	This function controls the number of Irreducible Infeasible Sets to be found using the iISAll ( IIS -a).
	MaxImpliedBound	Presolve: When tighter bounds are calculated during MIP preprocessing, only bounds whose absolute value are smaller than MAXIMPLIEDBOUND will be applied to the problem.
	MaxKappa	Largest basis condition number (also known as kappa) calculated through all nodes sampled by MIPKAPPAFREQ.
	MaxLocalBacktrack	Branch-and-Bound: How far back up the current dive path the optimizer is allowed to look for a local backtrack candidate node.
	MaxMCoeffBufferElems	The maximum number of matrix coefficients to buffer before flushing into the internal representation of the problem. Buffering coefficients can offer a significant performance gain when you are building a matrix using chgCoef or chgMCoef, but can lead to a significant memory overhead for large matrices, which this control allows you to influence.
	MaxMemoryHard	This control sets the maximum amount of memory in megabytes the optimizer should allocate. If this limit is exceeded, the solve will terminate. This control is designed to make the optimizer stop in a controlled manner, so that the problem object is valid once termination occurs. The solve state will be set to incomplete. This is different to an out of memory condition in which case the optimizer returns an error. The optimizer may still allocate memory once the limit is exceeded to be able to finsish the operations and stop in a controlled manner. When RESOURCESTRATEGY is enabled, the control also has the same effect as MAXMEMORYSOFT and will cause the optimizer to try preserving memory when possible.
	MaxMemorySoft	When RESOURCESTRATEGY is enabled, this control sets the maximum amount of memory in megabytes the optimizer targets to allocate. This may change the solving path, but will not cause the solve to terminate early. To set a hard version of the same, please set MAXMEMORYHARD.
	MaxMipInfeas	Maximum integer fractionality in the solution.
	MaxMIPSol	Branch and Bound: This specifies a limit on the number of integer solutions to be found by the Optimizer. It is possible that during optimization the Optimizer will find the same objective solution from different nodes. However, MAXMIPSOL refers to the total number of integer solutions found, and not necessarily the number of distinct solutions.
	MaxMipTasks	Branch-and-Bound: The maximum number of tasks to run in parallel during a MIP solve.
	MaxNode	Branch and Bound: The maximum number of nodes that will be explored.
	MaxPageLines	Number of lines between page breaks in printable output.
	MaxProbNameLength	Maximum size of the problem name and also the maximum allowed length of the file or path string for any function that accepts such an argument (not including the null terminator).
	MaxRelDualInfeas	Maximum calculated relative dual infeasibility in the unscaled original problem.
	MaxRelPrimalInfeas	Maximum calculated relative primal infeasibility in the unscaled original problem.
	MaxScaleFactor	This determines the maximum scaling factor that can be applied during scaling. The maximum is provided as an exponent of a power of 2.
	MaxStallTime	The maximum time in seconds that the Optimizer will continue to search for improving solution after finding a new incumbent.
	MaxTime	Obsolete. The maximum time in seconds that the Optimizer will run before it terminates, including the problem setup time and solution time. For MIP problems, this is the total time taken to solve all nodes.
	MaxTreeFileSize	The maximum size, in megabytes, to which the tree file may grow, or 0 for no limit. When the tree file reaches this limit, a second tree file will be created. Useful if you are using a filesystem that puts a maximum limit on the size of a file.
	MCFCutStrategy	Level of Multi-Commodity Flow (MCF) cutting planes separation: This specifies how much aggresively MCF cuts should be separated. If the separation of MCF cuts is enabled, Xpress will try to detect a MCF network structure in the problem and, if such a structure is identified, it will separate specific cutting planes exploiting the identified network.
	MemoryLimitDetected	The detected amount of memory accessible to the solver process, in megabytes. This is the minimum of physical memory, virtual memory limitations, and detected container limitations (Linux only).
	MIPAbsCutoff	Branch and Bound: If the user knows that they are interested only in values of the objective function which are better than some value, this can be assigned to MIPABSCUTOFF. This allows the Optimizer to ignore solving any nodes which may yield worse objective values, saving solution time. When a MIP solution is found a new cut off value is calculated and the value can be obtained from the CURRMIPCUTOFF attribute. The value of CURRMIPCUTOFF is calculated using the MIPRELCUTOFF and MIPADDCUTOFF controls.
	MIPAbsGapNotify	Branch and bound: if the gapnotify callback has been set using addCbGapNotify, then this callback will be triggered during the tree search when the absolute gap reaches or passes the value you set of the MIPRELGAPNOTIFY control.
	MIPAbsGapNotifyBound	Branch and bound: if the gapnotify callback has been set using addCbGapNotify, then this callback will be triggered during the tree search when the best bound reaches or passes the value you set of the MIPRELGAPNOTIFYBOUND control.
	MIPAbsGapNotifyObj	Branch and bound: if the gapnotify callback has been set using addCbGapNotify, then this callback will be triggered during the tree search when the best solution value reaches or passes the value you set of the MIPRELGAPNOTIFYOBJ control.
	MIPAbsStop	Branch and Bound: The absolute tolerance determining whether the tree search will continue or not. It will terminate if    \| MIPOBJVAL - BESTBOUND\| <= MIPABSSTOP where MIPOBJVAL is the value of the best solution's objective function, and BESTBOUND is the current best solution bound. For example, to stop the tree search when a MIP solution has been found and the Optimizer can guarantee it is within 100 of the optimal solution, set MIPABSSTOP to 100.
	MIPAddCutoff	Branch and Bound: The amount to add to the objective function of the best integer solution found to give the new CURRMIPCUTOFF. Once an integer solution has been found whose objective function is equal to or better than CURRMIPCUTOFF, improvements on this value may not be interesting unless they are better by at least a certain amount. If MIPADDCUTOFF is nonzero, it will be added to CURRMIPCUTOFF each time an integer solution is found which is better than this new value. This cuts off sections of the tree whose solutions would not represent substantial improvements in the objective function, saving processor time. The control MIPABSSTOP provides a similar function but works in a different way.
	MIPBestObjVal	Objective function value of the best integer solution found.
	MipComponents	Determines whether disconnected components in a MIP should be solved as separate MIPs. There can be significant performence benefits from solving disconnected components individual instead of being part of the main branch-and-bound search.
	MipConcurrentNodes	Sets the node limit for when a winning solve is selected when concurrent MIP solves are enabled. When multiple MIP solves are started, they each run up to the MIPCONCURRENTNODES node limit and only one winning solve is selected for contuinuing the search with.
	MipConcurrentSolves	Selects the number of concurrent solves to start for a MIP. Each solve will use a unique random seed for its random number generator, but will otherwise apply the same user controls. The first concurrent solve to complete will have solved the MIP and all the concurrent solves will be terminated at this point. Using concurrent solves can be advantageous when a MIP displays a high level of performance variability.
	MIPDualReductions	Branch and Bound: Limits operations that can reduce the MIP solution space.
	MIPEnts	Number of MIP entities (i.e. binary, integer, semi-continuous, partial integer, and semi-continuous integer variables) but excluding the number of special ordered sets.
	MipFracReduce	Branch and Bound: Specifies how often the optimizer should run a heuristic to reduce the number of fractional integer variables in the node LP solutions.
	MIPInfeas	Number of integer infeasibilities, including violations of special ordered sets, at the current node.
	MIPKappaFreq	Branch and Bound: Specifies how frequently the basis condition number (also known as kappa) should be calculated during the branch-and-bound search.
	MIPLog	MIP log print control.
	MIPObjVal	Objective function value of the last integer solution found.
	MIPPresolve	Branch and Bound: Type of integer processing to be performed. If set to 0, no processing will be performed.
	MipRampup	Controls the strategy used by the parallel MIP solver during the ramp-up phase of a branch-and-bound tree search.
	MipRefineIterLimit	This defines an effort limit expressed as simplex iterations for the MIP solution refiner. The limit is per reoptimizations in the MIP refiner.
	MIPRelCutoff	Branch and Bound: Percentage of the incumbent value to be added to the value of the objective function when an integer solution is found, to give the new value of CURRMIPCUTOFF. The effect is to cut off the search in parts of the tree whose best possible objective function would not be substantially better than the current solution. The control MIPRELSTOP provides a similar functionality but works in a different way.
	MIPRelGapNotify	Branch and bound: if the gapnotify callback has been set using addCbGapNotify, then this callback will be triggered during the branch and bound tree search when the relative gap reaches or passes the value you set of the MIPRELGAPNOTIFY control.
	MIPRelStop	Branch and Bound: This determines when the branch and bound tree search will terminate. Branch and bound tree search will stop if:    \| MIPOBJVAL - BESTBOUND\| <= MIPRELSTOP x max(\| BESTBOUND\|,\| MIPOBJVAL\|) where MIPOBJVAL is the value of the best solution's objective function and BESTBOUND is the current best solution bound. For example, to stop the tree search when a MIP solution has been found and the Optimizer can guarantee it is within 5% of the optimal solution, set MIPRELSTOP to 0.05.
	MipRestart	Branch and Bound: controls strategy for in-tree restarts.
	MIPRestartFactor	Branch and Bound: Fine tune initial conditions to trigger an in-tree restart. Use a value > 1 to increase the aggressiveness with which the Optimizer restarts. Use a value < 1 to relax the aggressiveness with which the Optimizer restarts. Note that this control does not affect the initial condition on the gap, which must be set separately.
	MIPRestartGapThreshold	Control MIPRESTARTGAPTHRESHOLD.
	MIPSolNode	Node at which the last integer feasible solution was found.
	MIPSols	Number of integer solutions that have been found.
	MIPSolTime	Time at which the last integer feasible solution was found.
	MIPStatus	(MIP) solution status.
	MipTerminationMethod	Branch and Bound: How a MIP solve should be stopped on early termination when there are still active tasks in the system. This can happen when, for example, a time or node limit is reached.
	MIPThreadID	The ID for the MIP thread.
	MIPThreads	If set to a positive integer it determines the number of threads implemented to run the parallel MIP code. If MIPTHREADS is set to the default value ( -1), the THREADS control will determine the number of threads used.
	MIPTol	Branch and Bound: This is the tolerance within which a decision variable's value is considered to be integral.
	MipTolTarget	Target MIPTOL value used by the automatic MIP solution refiner as defined by REFINEOPS. Negative and zero values are ignored.
	MIQCPAlg	This control determines which algorithm is to be used to solve mixed integer quadratic constrained and mixed integer second order cone problems.
	MPS18Compatible	Provides compatibility of MPS file output for older MPS readers.
	MPSBoundName	When reading an MPS file, this control determines which entries from the BOUNDS section will be read. As with all string controls, this is of length 64 characters plus a null terminator, \0.
	MPSEcho	Determines whether comments in MPS matrix files are to be printed out during matrix input.
	MPSFormat	Specifies the format of MPS files.
	MPSNameLength	Control MPSNAMELENGTH.
	MPSObjName	When reading an MPS file, this control determines which neutral row will be read as the objective function. If this control is set when reading a multi-objective MPS file, only the named objective will be read; all other objectives will be ignored. As with all string controls, this is of length 64 characters plus a null terminator, \0.
	MPSRangeName	When reading an MPS file, this control determines which entries from the RANGES section will be read. As with all string controls, this is of length 64 characters plus a null terminator, \0.
	MPSRHSName	When reading an MPS file, this control determines which entries from the RHS section will be read. As with all string controls, this is of length 64 characters plus a null terminator, \0.
	MsMaxBoundRange	Defines the maximum range inside which initial points are generated by multistart presets
	MultiObjLog	Log level for multi-objective optimization.
	MultiObjOps	Modifies the behaviour of the optimizer when solving multi-objective problems.
	MultiStart	The multistart master control. Defines if the multistart search is to be initiated, or if only the baseline model is to be solved.
	MultiStart_Log	The level of logging during the multistart run.
	MultiStart_MaxSolves	The maximum number of jobs to create during the multistart search.
	MultiStart_MaxTime	The maximum total time to be spent in the mutlistart search.
	MultiStart_PoolSize	The maximum number of problem objects allowed to pool up before synchronization in the deterministic multistart.
	MultiStart_Seed	Random seed used for the automatic generation of initial point when loading multistart presets
	MultiStart_Threads	The maximum number of threads to be used in multistart
	MutexCallBacks	Branch and Bound: This determines whether the callback routines are mutexed from within the optimizer.
	NameLength	The length (in 8 character units) of row and column names in the matrix. To allocate a character array to store names, you must allow 8*NAMELENGTH+1 characters per name (the +1 allows for the string terminator character).
	NetCuts	Obsolete. Control NETCUTS.
	NetStallLimit	Limit the number of degenerate pivots of the network simplex algorithm, before switching to either primal or dual simplex, depending on ALGAFTERNETWORK.
	NlpCalcThreads	Number of threads used for formula and derivatives evaluations
	NlpDefaultIV	Default initial value for an SLP variable if none is explicitly given
	NlpDerivatives	Bitmap describing the method of calculating derivatives
	NlpDeterministic	Determines if the parallel features of SLP should be guaranteed to be deterministic
	NlpEqualsColumn	Index of the reserved "=" column
	NlpEvaluate	Evaluation strategy for user functions
	NlpFindIV	Option for running a heuristic to find a feasible initial point
	NlpFuncEval	Bit map for determining the method of evaluating user functions and their derivatives
	NlpHessian	Second order differentiation mode when using analytical derivatives
	NlpIfs	Number of internal functions
	NlpImplicitVariables	Number of SLP variables appearing only in coefficients
	NlpInfinity	Value returned by a divide-by-zero in a formula
	NlpJacobian	First order differentiation mode when using analytical derivatives
	NlpJobID	Unique identifier for the current job
	NlpKeepBestIter	The iteration in which the returned solution has been found.
	NlpLinQuadBR	Use linear and quadratic constraints and objective function to further reduce bounds on all variables
	NlpLog	Level of printing during SLP iterations
	NlpMaxTime	The maximum time in seconds that the SLP optimization will run before it terminates
	NlpMeritLambda	Factor by which the net objective is taken into account in the merit function
	NlpModelCols	Number of model columns in the problem
	NlpModelRows	Number of model rows in the problem
	NlpObjVal	Objective function value excluding any penalty costs
	NlpOptTime	Time spent in optimization
	NlpOriginalCols	Number of model columns in the extended original problem
	NlpOriginalRows	Number of model rows in the extended original problem
	NlpPostsolve	This control determines whether postsolving should be performed automatically
	NlpPresolve	This control determines whether presolving should be performed prior to starting the main algorithm
	NlpPresolve_ElimTol	Tolerance for nonlinear eliminations during SLP presolve
	NlpPresolveEliminations	Number of SLP variables eliminated by XSLPpresolve
	NlpPresolveLevel	This control determines the level of changes presolve may carry out on the problem and whether column/row indices may change
	NlpPresolveOps	Bitmap indicating the SLP presolve actions to be taken
	NlpPresolveZero	Minimum absolute value for a variable which is identified as nonzero during SLP presolve
	NlpPrimalIntegralAlpha	Decay term for primal integral computation
	NlpPrimalIntegralRef	Reference solution value to take into account when calculating the primal integral
	NlpProbing	This control determines whether probing on a subset of variables should be performed prior to starting the main algorithm. Probing runs multiple times bound reduction in order to further tighten the bounding box.
	NlpReformulate	Controls the problem reformulations carried out before augmentation. This allows SLP to take advantage of dedicated algorithms for special problem classes.
	NLPSolver	Selects the library to use for local solves
	NlpStatus	Bitmap holding the problem convergence status
	NlpStopOutOfRange	Stop optimization and return error code if internal function argument is out of range
	NlpStopStatus	Status of the optimization process.
	NlpThreads	Default number of threads to be used
	NlpThreadSafeUserFunc	Defines if user functions are allowed to be called in parallel
	NlpUFs	Number of user functions
	NlpUseDerivatives	Indicates whether numeric or analytic derivatives were used to create the linear approximations and solve the problem
	NlpUserFuncCalls	Number of calls made to user functions
	NlpValidationFactor	Minimum improvement in validation targets to continue iterating
	NlpValidationIndex_A	Absolute validation index
	NlpValidationIndex_K	Relative first order optimality validation index
	NlpValidationIndex_R	Relative validation index
	NlpValidationTarget_K	Optimality target tolerance
	NlpValidationTarget_R	Feasiblity target tolerance
	NlpValidationTol_A	Absolute tolerance for the XSLPvalidate procedure
	NlpValidationTol_K	Relative tolerance for the XSLPvalidatekkt procedure
	NlpValidationTol_R	Relative tolerance for the XSLPvalidate procedure
	NlpVariables	Number of SLP variables
	NlpZero	Absolute tolerance
	NodeDepth	Depth of the current node.
	NodeProbingEffort	Adjusts the overall level of node probing.
	Nodes	Number of nodes solved so far in the MIP search. A node is counted as solved when it is either dropped or branched on.
	NodeSelection	Branch and Bound: This determines which nodes will be considered for solution once the current node has been solved.
	NonLinearConstraints	Number of nonlinear constraints in the problem
	NumericalEmphasis	How much emphasis to place on numerical stability instead of solve speed.
	NumIIS	Number of IISs found. You should first query the IISSOLSTATUS attribute to make sure that the IIS procedure terminated successfully.
	Objectives	Number of objectives in the problem.
	ObjectPointer	Returns the C language 'pointer' to the native object that this managed class is wrapping. For internal FICO use only. (Inherited from XPRSobject.)
	ObjName	Obsolete.
	ObjRHS	Fixed part of the objective function.
	ObjScaleFactor	Custom objective scaling factor, expressed as a power of 2. When set, it overwrites the automatic objective scaling factor. A value of 0 means no objective scaling. This control is applied for the full solve, and is independent of any extra scaling that may occur specifically for the barrier or simplex solvers. As it is a power of 2, to scale by 16, set the value of the control to 4.
	ObjSense	Get objective sense.
	OBJSense	Sense of the optimization being performed.
	ObjsToSolve	Number of objectives that will be solved during the current multi-objective solve.
	ObjVal	Value of the objective function of the last problem solved with optimize.
	ObservedPrimalIntegral	Value of the (observed) primal integral.
	OptimalityTol	Simplex: This is the zero tolerance for reduced costs. On each iteration, the simplex method searches for a variable to enter the basis which has a negative reduced cost. The candidates are only those variables which have reduced costs less than the negative value of OPTIMALITYTOL.
	OptimalityTolTarget	This specifies the target optimality tolerance for the solution refiner.
	OptimizeTypeUsed	The type of solver used in the last call to optimize, mipOptimize, lpOptimize or nlpOptimize.
	OriginalCols	Number of columns (i.e. variables) in the original matrix before presolving.
	OriginalGenconCols	Number of input variables in general constraints in the original problem before presolving.
	OriginalGencons	Number of general constraints in the original problem before presolving.
	OriginalGenconVals	Number of constant values in general constraints in the original problem before presolving.
	OriginalIndicators	Number of indicator constraints in the original matrix before presolving.
	OriginalMIPEnts	Number of MIP entities (i.e. binary, integer, semi-continuous, partial integer, and semi-continuous integer variables) but excluding the number of special ordered sets in the original matrix before presolving.
	OriginalPwlpoints	Number of breakpoints of piecewise linear constraints in the original problem before presolving.
	OriginalPwls	Number of piecewise linear constraints in the original problem before presolving.
	OriginalQCElems	Number of quadratic row coefficients in the original matrix before presolving.
	OriginalQConstraints	Number of rows with quadratic coefficients in the original matrix before presolving.
	OriginalQElems	Number of quadratic non-zeros in the original objective before presolving.
	OriginalRows	Number of rows (i.e. constraints) in the original matrix before presolving.
	OriginalSetMembers	Number of variables within special ordered sets (set members) in the original matrix before presolving.
	OriginalSets	Number of special ordered sets in the original matrix before presolving.
	OutputControls	This control toggles the printing of all control settings at the beginning of the search. This includes the printing of controls that have been explicitly assigned to their default value. All unset controls are omitted as they keep their default value.
	OutputLog	This controls the level of output produced by the Optimizer during optimization. In the Console Optimizer, OUTPUTLOG controls which messages are sent to the screen ( stdout). When using the Optimizer library, no output is sent to the screen. If the user wishes output to be displayed, they must define a callback function and print messages to the screen themselves. In this case, OUTPUTLOG controls which messages are sent to the user output callback.
	OutputMask	Mask to restrict the row and column names written to file. As with all string controls, this is of length 64 characters plus a null terminator, \0.
	OutputTol	Zero tolerance on print values.
	ParentNode	The parent node of the current node in the tree search.
	PeakMemory	An estimate of the peak amount of dynamically allocated heap memory by the problem.
	PeakTotalTreeMemoryUsage	The peak size, in megabytes, that the branch-and-bound search tree reached during the solve. Note that this value will include the uncompressed size of any compressed data and the size of any data saved to the tree file.
	Penalty	Minimum absolute penalty variable coefficient. BIGM and PENALTY are set by the input routine ( readProb ( READPROB)) but may be reset by the user prior to lpOptimize ( LPOPTIMIZE).
	PenaltyValue	The weighted sum of violations in the solution to the relaxed problem identified by the infeasibility repair function.
	PhysicalCoresDetected	The total number of physical cores across all CPUs detected by the optimizer.
	PhysicalCoresPerCPUDetected	The number of physical cores per CPU detected by the optimizer.
	PivotTol	Simplex: The zero tolerance for matrix elements. On each iteration, the simplex method seeks a nonzero matrix element to pivot on. Any element with absolute value less than PIVOTTOL is treated as zero for this purpose.
	PPFactor	The partial pricing candidate list sizing parameter.
	PreAnalyticcenter	Determines if analytic centers should be computed and used for variable fixing and the generation of alternative reduced costs (-1: Auto 0: Off, 1: Fixing, 2: Redcost, 3: Both)
	PreBasisRed	Determines if a lattice basis reduction algorithm should be attempted as part of presolve
	PreBndRedCone	Determines if second order cone constraints should be used for inferring bound reductions on variables when solving a MIP.
	PreBndRedQuad	Determines if convex quadratic constraints should be used for inferring bound reductions on variables when solving a MIP.
	PreCliqueStrategy	Determines how much effort to spend on clique covers in presolve.
	PreCoefElim	Presolve: Specifies whether the optimizer should attempt to recombine constraints in order to reduce the number of non zero coefficients when presolving a mixed integer problem.
	PreComponents	Presolve: determines whether small independent components should be detected and solved as individual subproblems during root node processing.
	PreComponentsEffort	Presolve: adjusts the overall effort for the independent component presolver. This control affects working limits for the subproblem solving as well as thresholds when it is called. Increase to put more emphasis on component presolving.
	PreConeDecomp	Presolve: decompose regular and rotated cones with more than two elements and apply Outer Approximation on the resulting components.
	PreConfiguration	MIP Presolve: determines whether binary rows with only few repeating coefficients should be reformulated. The reformulation enumerates the extremal feasible configurations of a row and introduces new columns and rows to model the choice between these extremal configurations. This presolve operation can be disabled as part of the (advanced) IP reductions PRESOLVEOPS.
	PreConvertObjToCons	Presolve: convert a linear or quadratic objective function into an objective transfer constraint
	PreConvertSeparable	Presolve: reformulate problems with a non-diagonal quadratic objective and/or constraints as diagonal quadratic or second-order conic constraints.
	PredictedAttLevel	A measure between 0 and 1 to predict how numerically unstable the current MIP solve can be expected to be. After the root LP solve, a machine learning model is used to predict the actual ATTENTIONLEVEL which will only be computed if MIPKAPPAFREQ is set to a nonzero value. If the predicted attention level exceeds a value of 0.1, a message will be printed to the log.
	PreDomCol	Presolve: Determines the level of dominated column removal reductions to perform when presolving a mixed integer problem. Only binary columns will be checked.
	PreDomRow	Presolve: Determines the level of dominated row removal reductions to perform when presolving a problem.
	PreDupRow	Presolve: Determines the type of duplicate rows to look for and eliminate when presolving a problem.
	PreElimQuad	Presolve: Allows for elimination of quadratic variables via doubleton rows.
	PreFolding	Presolve: Determines if a folding procedure should be used to aggregate continuous columns in an equitable partition.
	PreImplications	Presolve: Determines whether to use implication structures to remove redundant rows. If implication sequences are detected, they might also be used in probing.
	PreLinDep	Presolve: Determines whether to check for and remove linearly dependent equality constraints when presolving a problem.
	PreObjCutDetect	Presolve: Determines whether to check for constraints that are parallel or near parallel to a linear objective function, and which can safely be removed. This reduction applies to MIPs only.
	PrePermute	This bit vector control specifies whether to randomly permute rows, columns and MIP entities when starting the presolve. With the default value 0, no permutation will take place.
	PrePermuteSeed	This control sets the seed for the pseudo-random number generator for permuting the problem when starting the presolve. This control only has effects when PREPERMUTE is enabled.
	PreProbing	Presolve: Amount of probing to perform on binary variables during presolve. This is done by fixing a binary to each of its values in turn and analyzing the implications.
	PreProtectDual	Presolve: specifies whether the presolver should protect a given dual solution by maintaining the same level of dual feasibility. Enabling this control often results in a worse presolved model. This control only expected to be optionally enabled before calling crossoverLpSol.
	Presolve	This control determines whether presolving should be performed prior to starting the main algorithm. Presolve attempts to simplify the problem by detecting and removing redundant constraints, tightening variable bounds, etc. In some cases, infeasibility may even be determined at this stage, or the optimal solution found.
	PresolveIndex	Presolve: The row or column index on which presolve detected a problem to be infeasible or unbounded.
	PresolveMaxGrow	Limit on how much the number of non-zero coefficients is allowed to grow during presolve, specified as a ratio of the number of non-zero coefficients in the original problem.
	PresolveOps	This bit vector control specifies the operations which are performed during the presolve.
	PresolvePasses	Number of reduction rounds to be performed in presolve
	PresolveState	Problem status as a bit map.
	PreSort	This bit vector control specifies whether to sort rows, columns and MIP entities by their names when starting the presolve. With the default value 0, no sorting will take place.
	PricingAlg	Simplex: This determines the primal simplex pricing method. It is used to select which variable enters the basis on each iteration. In general Devex pricing requires more time on each iteration, but may reduce the total number of iterations, whereas partial pricing saves time on each iteration, but may result in more iterations.
	PrimalDualIntegral	Value of the primal-dual integral.
	PrimalInfeas	Number of primal infeasibilities.
	PrimalOps	Primal simplex: allows fine tuning the variable selection in the primal simplex solver.
	PrimalPerturb	The factor by which the problem will be perturbed prior to optimization by primal simplex. A value of 0.0 results in no perturbation prior to optimization. Note the interconnection to the AUTOPERTURB control. If AUTOPERTURB is set to 1, the decision whether to perturb or not is left to the Optimizer. When the problem is automatically perturbed in primal simplex, however, the value of PRIMALPERTURB will be used for perturbation.
	PrimalUnshift	Determines whether primal is allowed to call dual to unshift.
	PseudoCost	Branch and Bound: The default pseudo cost used in estimation of the degradation associated with an unexplored node in the tree search. A pseudo cost is associated with each integer decision variable and is an estimate of the amount by which the objective function will be worse if that variable is forced to an integral value.
	PwlCons	Number of piecewise linear constraints in the problem.
	PwlDualReductions	This parameter specifies whether dual reductions should be applied to reduce the number of columns, rows and SOS-constraints added when transforming piecewise linear objectives and constraints to MIP structs.
	PwlNonConvexTransformation	This control specifies the reformulation method for piecewise linear constraints at the beginning of the search. Note that the chosen formulation will only be used if MIP entities are necessary but not if presolve detected that a convex reformulation is possible. Furthermore, the binary formulation will only be applied to piecewise linear constraints with bounded input variable, otherwise the SOS2-formulation will be used.
	PwlPoints	Number of breakpoints of piecewise linear constraints in the problem.
	QCCuts	Branch and Bound: Limit on the number of rounds of outer approximation cuts generated for the root node, when solving a mixed integer quadratic constrained or mixed integer second order conic problem with outer approximation.
	QCElems	Number of quadratic row coefficients in the matrix.
	QConstraints	Number of rows with quadratic coefficients in the matrix.
	QCRootAlg	This control determines which algorithm is to be used to solve the root of a mixed integer quadratic constrained or mixed integer second order cone problem, when outer approximation is used.
	QElems	Number of quadratic non-zeros in the objective.
	QSimplexOps	Controls the behavior of the quadratic simplex solvers.
	QuadraticUnshift	Determines whether an extra solution purification step is called after a solution found by the quadratic simplex (either primal or dual).
	RandomSeed	Sets the initial seed to use for the pseudo-random number generator in the Optimizer. The sequence of random numbers is always reset using the seed when starting a new optimization run.
	RangeName	Active range name.
	Refactor	Indicates whether the optimization should restart using the current representation of the factorization in memory.
	RefineOps	This specifies when the solution refiner should be executed to reduce solution infeasibilities. The refiner will attempt to satisfy the target tolerances for all original linear constraints before presolve or scaling has been applied.
	RelaxTreeMemoryLimit	When the memory used by the branch and bound search tree exceeds the target specified by the TREEMEMORYLIMIT control, the optimizer will try to reduce this by writing nodes to the tree file. In rare cases, usually where the solve has many millions of very small nodes, the tree structural data (which cannot be written to the tree file) will grow large enough to approach or exceed the tree's memory target. When this happens, optimizer performance can degrade greatly as the solver makes heavy use of the tree file in preference to memory. To prevent this, the solver will automatically relax the tree memory limit when it detects this case; the RELAXTREEMEMORYLIMIT control specifies the proportion of the previous memory limit by which to relax it. Set RELAXTREEMEMORYLIMIT to 0.0 to force the Xpress Optimizer to never relax the tree memory limit in this way.
	RelPivotTol	Simplex: At each iteration a pivot element is chosen within a given column of the matrix. The relative pivot tolerance, RELPIVOTTOL, is the size of the element chosen relative to the largest possible pivot element in the same column.
	RepairIndefiniteQ	Controls if the optimizer should make indefinite quadratic matrices positive definite when it is possible.
	RepairIndefiniteQMax	Control REPAIRINDEFINITEQMAX.
	RepairInfeasMaxTime	Obsolete. Overall time limit for the repairinfeas tool
	RepairInfeasTimeLimit	Overall time limit for the repairinfeas tool
	ResourceStrategy	Controls whether the optimizer is allowed to make nondeterministic decisions if memory is running low in an effort to preserve memory and finish the solve. Available memory (or container limits) are automatically detected but can also be changed by MAXMEMORYSOFT and MAXMEMORYHARD
	Restarts	Total number of restarts performed.
	RHSName	Active right hand side name.
	RLTCuts	Determines whether RLT cuts should be separated in the Xpress Global Solver.
	RootPresolve	Determines if presolving should be performed on the problem after the tree search has finished with root cutting and heuristics.
	Rows	Number of rows (i.e. constraints) in the matrix.
	SBBest	Number of infeasible MIP entities to initialize pseudo costs for on each node.
	SbEffort	Adjusts the overall amount of effort when using strong branching to select an infeasible MIP entity to branch on.
	SBEstimate	Branch and Bound: How to calculate pseudo costs from the local node when selecting an infeasible MIP entity to branch on. These pseudo costs are used in combination with local strong branching and history costs to select the branch candidate.
	SBIterLimit	Number of dual iterations to perform the strong branching for each entity.
	SBSelect	The size of the candidate list of MIP entities for strong branching.
	Scaling	This bit vector control determines how the Optimizer will rescale a model internally before optimization. If set to 0, no scaling will take place.
	SerializePreIntSol	Setting SERIALIZEPREINTSOL to 1 will ensure that the preintsol callback is always fired in a deterministic order during a parallel MIP solve. This applies only when the control DETERMINISTIC is set to 1.
	SetMembers	Number of variables within special ordered sets (set members) in the matrix.
	Sets	Number of special ordered sets in the matrix.
	Sifting	Determines whether to enable sifting algorithm with the dual simplex method.
	SiftPasses	Determines how quickly we allow to grow the worker problems during the sifting algorithm. Using larger values can increase the number of columns added to the worker problem which often results in increased solve times for the worker problems but the number of necessary sifting iterations may be reduced. .
	SiftPresolveOps	Determines the presolve operations for solving the subproblems during the sifting algorithm.
	SiftSwitch	Determines which algorithm to use for solving the subproblems during sifting.
	SimplexIter	Number of simplex iterations performed.
	SleepOnThreadWait	Obsolete. In previous versions this was used to determine if the threads should be put into a wait state when waiting for work.
	SlpAlgorithm	Bit map describing the SLP algorithm(s) to be used
	SlpAnalyze	Bit map activating additional options supporting model / solution path analyzis
	SlpATol_A	Absolute delta convergence tolerance
	SlpATol_R	Relative delta convergence tolerance
	SlpAugmentation	Bit map describing the SLP augmentation method(s) to be used
	SlpAutoSave	Frequency with which to save the model
	SlpBarCrossoverStart	Default crossover activation behaviour for barrier start
	SlpBarLimit	Number of initial SLP iterations using the barrier method
	SlpBarStallingLimit	Number of iterations to allow numerical failures in barrier before switching to dual
	SlpBarStallingObjLimit	Number of iterations over which to measure the objective change for barrier iterations with no crossover
	SlpBarStallingTol	Required change in the objective when progress is measured in barrier iterations without crossover
	SlpBarStartOps	Controls behaviour when the barrier is used to solve the linearizations
	SlpBoundThreshold	The maximum size of a bound that can be introduced by nonlinear presolve.
	SlpCascade	Bit map describing the cascading to be used
	SlpCascadeNLimit	Maximum number of iterations for cascading with non-linear determining rows
	SlpCascadeTol_PA	Absolute cascading print tolerance
	SlpCascadeTol_PR	Relative cascading print tolerance
	SlpCDTol_A	Absolute tolerance for deducing constant derivatives
	SlpCDTol_R	Relative tolerance for deducing constant derivatives
	SlpClampShrink	Shrink ratio used to impose strict convergence on variables converged in extended criteria only
	SlpClampValidationTol_A	Absolute validation tolerance for applying XSLP_CLAMPSHRINK
	SlpClampValidationTol_R	Relative validation tolerance for applying XSLP_CLAMPSHRINK
	SlpCoefficients	Number of nonlinear coefficients
	SlpConvergenceOps	Bit map describing which convergence tests should be carried out
	SlpCTol	Closure convergence tolerance
	SlpCurrentDeltaCost	Current value of penalty cost multiplier for penalty delta vectors
	SlpCurrentErrorCost	Current value of penalty cost multiplier for penalty error vectors
	SlpCutStrategy	Determines whihc cuts to apply in the MISLP search when the default SLP-in-MIP strategy is used.
	SlpDamp	Damping factor for updating values of variables
	SlpDampExpand	Multiplier to increase damping factor during dynamic damping
	SlpDampMax	Maximum value for the damping factor of a variable during dynamic damping
	SlpDampMin	Minimum value for the damping factor of a variable during dynamic damping
	SlpDampShrink	Multiplier to decrease damping factor during dynamic damping
	SlpDampStart	SLP iteration at which damping is activated
	SlpDefaultStepBound	Minimum initial value for the step bound of an SLP variable if none is explicitly given
	SlpDelayUpdateRows	Number of SLP iterations before update rows are fully activated
	SlpDelta_A	Absolute perturbation of values for calculating numerical derivatives
	SlpDelta_Infinity	Maximum value for partial derivatives
	SlpDelta_R	Relative perturbation of values for calculating numerical derivatives
	SlpDelta_X	Minimum absolute value of delta coefficients to be retained
	SlpDelta_Z	Tolerance used when calculating derivatives
	SlpDelta_Zero	Absolute zero acceptance tolerance used when calculating derivatives
	SlpDeltaCost	Initial penalty cost multiplier for penalty delta vectors
	SlpDeltaCostFactor	Factor for increasing cost multiplier on total penalty delta vectors
	SlpDeltaMaxCost	Maximum penalty cost multiplier for penalty delta vectors
	SlpDeltaOffset	Position of first character of SLP variable name used to create name of delta vector
	SlpDeltas	Number of delta vectors created during augmentation
	SlpDeltaZLimit	Number of SLP iterations during which to apply XSLP_DELTA_Z
	SlpDJTol	Tolerance on DJ value for determining if a variable is at its step bound
	SlpDRColDjTol	Reduced cost tolerance on the delta variable when fixing due to the determining column being below XSLP_DRCOLTOL.
	SlpDRColTol	The minimum absolute magnitude of a determining column, for which the determined variable is still regarded as well defined
	SlpDRFixRange	The range around the previous value where variables are fixed in cascading if the determining column is below XSLP_DRCOLTOL.
	SlpECFCheck	Check feasibility at the point of linearization for extended convergence criteria
	SlpECFCount	Number of infeasible constraints found at the point of linearization
	SlpEcfTol_A	Absolute tolerance on testing feasibility at the point of linearization
	SlpEcfTol_R	Relative tolerance on testing feasibility at the point of linearization
	SlpEnforceCostShrink	Factor by which to decrease the current penalty multiplier when enforcing rows.
	SlpEnforceMaxCost	Maximum penalty cost in the objective before enforcing most violating rows
	SlpErrorCost	Initial penalty cost multiplier for penalty error vectors
	SlpErrorCostFactor	Factor for increasing cost multiplier on total penalty error vectors
	SlpErrorCosts	Total penalty costs in the solution
	SlpErrorMaxCost	Maximum penalty cost multiplier for penalty error vectors
	SlpErrorOffset	Position of first character of constraint name used to create name of penalty error vectors
	SlpErrorTol_A	Absolute tolerance for error vectors
	SlpErrorTol_P	Absolute tolerance for printing error vectors
	SlpEscalation	Factor for increasing cost multiplier on individual penalty error vectors
	SlpETol_A	Absolute tolerance on penalty vectors
	SlpETol_R	Relative tolerance on penalty vectors
	SlpEVTol_A	Absolute tolerance on total penalty costs
	SlpEVTol_R	Relative tolerance on total penalty costs
	SlpExpand	Multiplier to increase a step bound
	SlpFeastolTarget	When set, this defines a target feasibility tolerance to which the linearizations are solved to
	SlpFilter	Bit map for controlling solution updates
	SlpGranularity	Base for calculating penalty costs
	SlpGridHeurSelect	Bit map selectin which heuristics to run if the problem has variable with an integer delta
	SlpHeurStrategy	Branch and Bound: This specifies the MINLP heuristic strategy. On some problems it is worth trying more comprehensive heuristic strategies by setting HEURSTRATEGY to 2 or 3.
	SlpInfeasLimit	The maximum number of consecutive infeasible SLP iterations which can occur before Xpress-SLP terminates
	SlpIter	SLP iteration count
	SlpIterLimit	The maximum number of SLP iterations
	SlpItol_A	Absolute impact convergence tolerance
	SlpITol_R	Relative impact convergence tolerance
	SlpLSIterLimit	Number of iterations in the line search
	SlpLSPatternLimit	Number of iterations in the pattern search preceding the line search
	SlpLSStart	Iteration in which to active the line search
	SlpLSZeroLimit	Maximum number of zero length line search steps before line search is deactivated
	SlpMatrixTol	Nonzero tolerance for dropping coefficients from the linearization.
	SlpMaxWeight	Maximum penalty weight for delta or error vectors
	SlpMinSBFactor	Factor by which step bounds can be decreased beneath XSLP_ATOL_A
	SlpMinusPenaltyErrors	Number of negative penalty error vectors
	SlpMinWeight	Minimum penalty weight for delta or error vectors
	SlpMipAlgorithm	Bitmap describing the MISLP algorithms to be used
	SlpMipCutoff_A	Absolute objective function cutoff for MIP termination
	SlpMipCutoff_R	Absolute objective function cutoff for MIP termination
	SlpMipCutOffCount	Number of SLP iterations to check when considering a node for cutting off
	SlpMipCutoffLimit	Number of SLP iterations to check when considering a node for cutting off
	SlpMipDefaultAlgorithm	Default algorithm to be used during the tree search in MISLP
	SlpMipErrorTol_A	Absolute penalty error cost tolerance for MIP cut-off
	SlpMipErrorTol_R	Relative penalty error cost tolerance for MIP cut-off
	SlpMipFixStepBounds	Bitmap describing the step-bound fixing strategy during MISLP
	SlpMipIter	Total number of SLP iterations in MISLP
	SlpMipIterLimit	Maximum number of SLP iterations at each node
	SlpMipLog	Frequency with which MIP status is printed
	SlpMipNodes	Number of nodes explored in MISLP. This includes any nodes for which a non-linear solve has been carried out.
	SlpMipOCount	Number of SLP iterations at each node over which to measure objective function variation
	SlpMipOtol_A	Absolute objective function tolerance for MIP termination
	SlpMipOtol_R	Relative objective function tolerance for MIP termination
	SlpMipRelaxStepBounds	Bitmap describing the step-bound relaxation strategy during MISLP
	SlpMipSols	Number of integer solutions found in MISLP. This includes solutions found during the tree search or any heuristics.
	SlpMTol_A	Absolute effective matrix element convergence tolerance
	SlpMTol_R	Relative effective matrix element convergence tolerance
	SlpMVTol	Marginal value tolerance for determining if a constraint is slack
	SlpNonConstantCoeffs	Number of coefficients in the augmented problem that might change between SLP iterations
	SlpObjThreshold	Assumed maximum value of the objective function in absolute value.
	SlpObjToPenaltyCost	Factor to estimate initial penalty costs from objective function
	SlpOCount	Number of SLP iterations over which to measure objective function variation for static objective (2) convergence criterion
	SlpOptimalityTolTarget	When set, this defines a target optimality tolerance to which the linearizations are solved to
	SlpOTol_A	Absolute static objective (2) convergence tolerance
	SlpOTol_R	Relative static objective (2) convergence tolerance
	SlpPenaltyDeltaColumn	Index of column costing the penalty delta row
	SlpPenaltyDeltaRow	Index of equality row holding the penalties for delta vectors
	SlpPenaltyDeltas	Number of penalty delta vectors
	SlpPenaltyDeltaTotal	Total activity of penalty delta vectors
	SlpPenaltyDeltaValue	Total penalty cost attributed to penalty delta vectors
	SlpPenaltyErrorColumn	Index of column costing the penalty error row
	SlpPenaltyErrorRow	Index of equality row holding the penalties for penalty error vectors
	SlpPenaltyErrors	Number of penalty error vectors
	SlpPenaltyErrorTotal	Total activity of penalty error vectors
	SlpPenaltyErrorValue	Total penalty cost attributed to penalty error vectors
	SlpPenaltyInfoStart	Iteration from which to record row penalty information
	SlpPlusPenaltyErrors	Number of positive penalty error vectors
	SlpSameCount	Number of steps reaching the step bound in the same direction before step bounds are increased
	SlpSameDamp	Number of steps in same direction before damping factor is increased
	SlpSBRowOffset	Position of first character of SLP variable name used to create name of SLP lower and upper step bound rows
	SlpSBStart	SLP iteration after which step bounds are first applied
	SlpSbxConverged	Number of step-bounded variables converged only on extended criteria
	SlpShrink	Multiplier to reduce a step bound
	SlpShrinkBias	Defines an overwrite / adjustment of step bounds for improving iterations
	SlpStatus	Bitmap holding the problem convergence status
	SlpSTol_A	Absolute slack convergence tolerance
	SlpSTol_R	Relative slack convergence tolerance
	SlpTolSets	Number of tolerance sets.
	SlpTraceMaskOps	Controls the information printed for XSLP_TRACEMASK. The order in which the information is printed is determined by the order of bits in XSLP_TRACEMASKOPS.
	SlpUCConstrainedCount	Number of unconverged variables with coefficients in constraining rows
	SlpUnConverged	Number of unconverged values
	SlpUnFinishedLimit	The number of consecutive SLP iterations that may have an unfinished status before the solve is terminated.
	SlpUpdateOffset	Position of first character of SLP variable name used to create name of SLP update row
	SlpVCount	Number of SLP iterations over which to measure static objective (3) convergence
	SlpVLimit	Number of SLP iterations after which static objective (3) convergence testing starts
	SlpVTol_A	Absolute static objective (3) convergence tolerance
	SlpVTol_R	Relative static objective (3) convergence tolerance
	SlpWCount	Number of SLP iterations over which to measure the objective for the extended convergence continuation criterion
	SlpWTol_A	Absolute extended convergence continuation tolerance
	SlpWTol_R	Relative extended convergence continuation tolerance
	SlpXCount	Number of SLP iterations over which to measure static objective (1) convergence
	SlpXLimit	Number of SLP iterations up to which static objective (1) convergence testing starts
	SlpXTol_A	Absolute static objective function (1) tolerance
	SlpXTol_R	Relative static objective function (1) tolerance
	SlpZeroCriterion	Bitmap determining the behavior of the placeholder deletion procedure
	SlpZeroCriterionCount	Number of consecutive times a placeholder entry is zero before being considered for deletion
	SlpZeroCriterionStart	SLP iteration at which criteria for deletion of placeholder entries are first activated.
	SlpZeroesReset	Number of placeholder entries set to zero
	SlpZeroesRetained	Number of potentially zero placeholders left untouched
	SlpZeroesTotal	Number of potential zero placeholder entries
	SolStatus	Status of the solution of the last problem solved with optimize.
	SolTimeLimit	The maximum time in seconds that the Optimizer will run a MIP solve before it terminates, given that a solution has been found. As long as no solution has been found, this control will have no effect.
	SolvedObjs	Number of objectives that have been solved so far during a multi-objective solve.
	SolveStatus	Status of the solve of the last problem solved with optimize.
	SOSRefTol	The minimum relative gap between the ordering values of elements in a special ordered set. The gap divided by the absolute value of the larger of the two adjacent values must be at least SOSREFTOL.
	SpareCols	Number of spare columns in the matrix.
	SpareElems	Number of spare matrix elements in the matrix.
	SpareMIPEnts	Number of spare MIP entities in the matrix.
	SpareRows	Number of spare rows in the matrix.
	SpareSetElems	Number of spare set elements in the matrix.
	SpareSets	Number of spare sets in the matrix.
	StopStatus	Status of the optimization process.
	SumPrimalInf	Scaled sum of primal infeasibilities.
	Symmetry	Adjusts the overall amount of effort for symmetry detection.
	SymSelect	Adjusts the overall amount of effort for symmetry detection.
	SystemMemory	The amount of non problem specific memory used by the solver.
	Threads	The default number of threads used during optimization.
	Time	Time spent solving the problem as measured by the optimizer.
	TimeLimit	The maximum time in seconds that the Optimizer will run before it terminates, including the problem setup time and solution time. For MIP problems, this is the total time taken to solve all nodes.
	TotalMemory	The amount of dynamically allocated heap memory by the optimizer, including all problems currently exsisting.
	Trace	Display the infeasibility diagnosis during presolve. If non-zero, an explanation of the logical deductions made by presolve to deduce infeasibility or unboundedness will be displayed on screen or sent to the message callback function.
	TreeCompletion	Estimation of the relative completion of the search tree as fraction between 0 and 1. Its accuracy mainly depends on the level of degeneracy of a problem and the balancedness of the search tree.
	TreeCompression	When writing nodes to the gloal file, the optimizer can try to use data-compression techniques to reduce the size of the tree file on disk. The TREECOMPRESSION control determines the strength of the data-compression algorithm used; higher values give superior data-compression at the affect of decreasing performance, while lower values compress quicker but not as effectively. Where TREECOMPRESSION is set to 0, no data compression will be used on the tree file.
	TreeCoverCuts	Branch and Bound: The number of rounds of lifted cover inequalities generated at nodes other than the top node in the tree. Compare with the description for COVERCUTS. A value of -1 indicates the number of rounds is determined automatically.
	TreeCutSelect	A bit vector providing detailed control of the cuts created during the tree search of a MIP solve. Use CUTSELECT to control cuts on the root node.
	TreeDiagnostics	A bit vector providing control over how various tree-management-related messages get printed in the tree log file during the branch-and-bound search.
	TreeFileLogInterval	This control sets the interval between progress messages output while writing tree data to the tree file, in seconds. The solve is slowed greatly while data is being written to the tree file and this output allows the user to see how much progress is being made.
	TreeFileSize	The allocated size of the tree file, in megabytes. Because data can be removed from the tree file during the branch and bound search, the size of the tree file is usually greater than the amount of data currently within it (represented by the TREEFILEUSAGE attribute).
	TreeFileUsage	The number of megabytes of data from the branch-and-bound tree that have been saved to the tree file. Note that the actual allocated size of the tree file (represented by the TREEFILESIZE attribute) may be greater than this value.
	TreeGomCuts	Branch and Bound: The number of rounds of Gomory cuts generated at nodes other than the first node in the tree. Compare with the description for GOMCUTS. A value of -1 indicates the number of rounds is determined automatically.
	TreeMemoryLimit	A soft limit, in megabytes, for the amount of memory to use in storing the branch and bound search tree. This doesn't include memory used for presolve, heuristics, solving the LP relaxation, etc. When set to 0 (the default), the optimizer will calculate a limit automatically based on the amount of free physical memory detected in the machine. When the memory used by the branch and bound tree exceeds this limit, the optimizer will try to reduce the memory usage by writing lower-rated sections of the tree to a file called the "tree file". Though the solve can continue if it cannot bring the tree memory usage below the specified limit, performance will be inhibited and a message will be printed to the log.
	TreeMemorySavingTarget	When the memory used by the branch-and-bound search tree exceeds the limit specified by the TREEMEMORYLIMIT control, the optimizer will try to save memory by writing lower-rated sections of the tree to the tree file. The target amount of memory to save will be enough to bring memory usage back below the limit, plus enough extra to give the tree room to grow. The TREEMEMORYSAVINGTARGET control specifies the extra proportion of the tree's size to try to save; for example, if the tree memory limit is 1000Mb and TREEMEMORYSAVINGTARGET is 0.1, when the tree size exceeds 1000Mb the optimizer will try to reduce the tree size to 900Mb. Reducing the value of TREEMEMORYSAVINGTARGET will cause less extra nodes of the tree to be written to the tree file, but will result in the memory saving routine being triggered more often (as the tree will have less room in which to grow), which can reduce performance. Increasing the value of TREEMEMORYSAVINGTARGET will cause additional, more highly-rated nodes, of the tree to be written to the tree file, which can cause performance issues if these nodes are required later in the solve.
	TreeMemoryUsage	The amount of physical memory, in megabytes, currently being used to store the branch-and-bound search tree.
	TreeQCCuts	Branch and Bound: Limit on the number of rounds of outer approximation cuts generated for nodes other than the root node, when solving a mixed integer quadratic constrained or mixed integer second order conic problem with outer approximation.
	TreeRestarts	Number of in-tree restarts performed.
	TunerHistory	Tuner: Whether to reuse and append to previous tuner results of the same problem.
	TunerMaxTime	Tuner: The maximum time in seconds that the tuner will run before it terminates.
	TunerMethod	Tuner: Selects a factory tuner method. A tuner method consists of a list of controls with different settings that the tuner will evaluate and try to combine.
	TunerMethodFile	Tuner: Defines a file from which the tuner can read user-defined tuner method.
	TunerMode	Tuner: Whether to always enable the tuner or disable it.
	TunerOutput	Tuner: Whether to output tuner results and logs to the file system.
	TunerOutputPath	Tuner: Defines a root path to which the tuner writes the result file and logs.
	TunerPermute	Tuner: Defines the number of permutations to solve for each control setting.
	TunerSessionName	Tuner: Defines a session name for the tuner.
	TunerTarget	Tuner: Defines the tuner target -- what should be evaluated when comparing two runs with different control settings.
	TunerThreads	Tuner: the number of threads used by the tuner.
	TunerVerbose	Tuner: whether the tuner should prints detailed information for each run.
	UserSolHeuristic	Determines how much effort to put into running a local search heuristic to find a feasible integer solution from a partial or infeasible user solution.
	UUID	Universally Unique Identifier for the problem instance.
	VarSelection	Branch and Bound: This determines the formula used to calculate the estimate of each integer variable, and thus which integer variable is selected to be branched on at a given node. The variable selected to be branched on is the one with the maximum estimate.
	Version	The Optimizer version number, e.g. 1301 meaning release 13.01.
	XpressVersion	The Xpress version number.

Top

Methods

	Name	Description
	AddAfterObjectiveCallback(AfterObjectiveCallback)	Add AfterObjective callback.
	AddAfterObjectiveCallback(AfterObjectiveCallback, Int32)	Add AfterObjective callback with the specified priority.
	AddAfterObjectiveCallback(AfterObjectiveCallback, Object)	Add AfterObjective callback with a user object that is passed to the callback on invocation.
	AddAfterObjectiveCallback(AfterObjectiveCallback, Object, Int32)	Add AfterObjective callback with specified priority and a user object that is passed to the callback on invocation.
	AddBarIterationCallback(BarIterationCallback)	Add BarIteration callback.
	AddBarIterationCallback(BarIterationCallback, Int32)	Add BarIteration callback with the specified priority.
	AddBarIterationCallback(BarIterationCallback, Object)	Add BarIteration callback with a user object that is passed to the callback on invocation.
	AddBarIterationCallback(BarIterationCallback, Object, Int32)	Add BarIteration callback with specified priority and a user object that is passed to the callback on invocation.
	AddBarlogCallback(BarlogCallback)	Add Barlog callback.
	AddBarlogCallback(BarlogCallback, Int32)	Add Barlog callback with the specified priority.
	AddBarlogCallback(BarlogCallback, Object)	Add Barlog callback with a user object that is passed to the callback on invocation.
	AddBarlogCallback(BarlogCallback, Object, Int32)	Add Barlog callback with specified priority and a user object that is passed to the callback on invocation.
	AddBeforeObjectiveCallback(BeforeObjectiveCallback)	Add BeforeObjective callback.
	AddBeforeObjectiveCallback(BeforeObjectiveCallback, Int32)	Add BeforeObjective callback with the specified priority.
	AddBeforeObjectiveCallback(BeforeObjectiveCallback, Object)	Add BeforeObjective callback with a user object that is passed to the callback on invocation.
	AddBeforeObjectiveCallback(BeforeObjectiveCallback, Object, Int32)	Add BeforeObjective callback with specified priority and a user object that is passed to the callback on invocation.
	AddBeforeSolveCallback(BeforeSolveCallback)	Add BeforeSolve callback.
	AddBeforeSolveCallback(BeforeSolveCallback, Int32)	Add BeforeSolve callback with the specified priority.
	AddBeforeSolveCallback(BeforeSolveCallback, Object)	Add BeforeSolve callback with a user object that is passed to the callback on invocation.
	AddBeforeSolveCallback(BeforeSolveCallback, Object, Int32)	Add BeforeSolve callback with specified priority and a user object that is passed to the callback on invocation.
	AddChangeBranchObjectCallback(ChangeBranchObjectCallback)	Add ChangeBranchObject callback.
	AddChangeBranchObjectCallback(ChangeBranchObjectCallback, Int32)	Add ChangeBranchObject callback with the specified priority.
	AddChangeBranchObjectCallback(ChangeBranchObjectCallback, Object)	Add ChangeBranchObject callback with a user object that is passed to the callback on invocation.
	AddChangeBranchObjectCallback(ChangeBranchObjectCallback, Object, Int32)	Add ChangeBranchObject callback with specified priority and a user object that is passed to the callback on invocation.
	AddCheckTimeCallback(CheckTimeCallback)	Add CheckTime callback.
	AddCheckTimeCallback(CheckTimeCallback, Int32)	Add CheckTime callback with the specified priority.
	AddCheckTimeCallback(CheckTimeCallback, Object)	Add CheckTime callback with a user object that is passed to the callback on invocation.
	AddCheckTimeCallback(CheckTimeCallback, Object, Int32)	Add CheckTime callback with specified priority and a user object that is passed to the callback on invocation.
	AddChgbranchCallback(ChgbranchCallback)	Obsolete. Add Chgbranch callback.
	AddChgbranchCallback(ChgbranchCallback, Int32)	Obsolete. Add Chgbranch callback with the specified priority.
	AddChgbranchCallback(ChgbranchCallback, Object)	Obsolete. Add Chgbranch callback with a user object that is passed to the callback on invocation.
	AddChgbranchCallback(ChgbranchCallback, Object, Int32)	Obsolete. Add Chgbranch callback with specified priority and a user object that is passed to the callback on invocation.
	AddChgnodeCallback(ChgnodeCallback)	Obsolete. Add Chgnode callback.
	AddChgnodeCallback(ChgnodeCallback, Int32)	Obsolete. Add Chgnode callback with the specified priority.
	AddChgnodeCallback(ChgnodeCallback, Object)	Obsolete. Add Chgnode callback with a user object that is passed to the callback on invocation.
	AddChgnodeCallback(ChgnodeCallback, Object, Int32)	Obsolete. Add Chgnode callback with specified priority and a user object that is passed to the callback on invocation.
	AddCol(Double, Double, Double)	Add a single column to this problem. This is a shortcut for AddCol(obj, lb, ub, 'C', null, null, null);.
	AddCol(Double, Double, Double, Char)	Add a single column to this problem. This is a shortcut for AddCol(obj, lb, ub, type, null, null, null);.
	AddCol(Double, Double, Double, String)	Add a single column to this problem. This is a shortcut for AddCol(obj, lb, ub, 'C', null, null, name);.
	AddCol(Double, Double, Double, Char, String)	Add a single column to this problem. This is a shortcut for AddCol(obj, lb, ub, type, null, null, name);.
	AddCol(Double, Double, Double, Char, Int32, Double)	Add a single column to this problem. This is a shortcut for AddCol(obj, lb, ub, type, rowind, rowval, null);.
	AddCol(Double, Double, Double, Char, Int32, Double, String)	Add a single column to the model.
	AddCols(Int32, Int32, Double, Int32, Int32, Double, Double, Double)	Adds columns to the optimizer matrix.
	AddCols(Int32, Int64, Double, Int64, Int32, Double, Double, Double)	Adds columns to the optimizer matrix.
	AddColumn	Add a single column to this problem.
	AddColumns(Int32)	Create an 1-dimensional array of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToArray() function. // Create a multi-dimensional array of binary columns int[ ] x = prob.AddColumns(dim) .WithType(Optimizer.Objects.ColumnType.Binary) .ToArray(); See Optimizer.VariableBuilder.ColumnArrayBuilder for details of how to modify the specification in the builder.
	AddColumns(Int32, Int32)	Create an 2-dimensional array of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToArray() function. // Create a multi-dimensional array of binary columns int[ ,] x = prob.AddColumns(dim1 ,dim2) .WithType(Optimizer.Objects.ColumnType.Binary) .ToArray(); See Optimizer.VariableBuilder.ColumnArray2Builder for details of how to modify the specification in the builder.
	AddColumns(Int32, Int32, Int32)	Create an 3-dimensional array of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToArray() function. // Create a multi-dimensional array of binary columns int[ , ,] x = prob.AddColumns(dim1 ,dim2 ,dim3) .WithType(Optimizer.Objects.ColumnType.Binary) .ToArray(); See Optimizer.VariableBuilder.ColumnArray3Builder for details of how to modify the specification in the builder.
	AddColumns(Int32, Int32, Int32, Int32)	Create an 4-dimensional array of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToArray() function. // Create a multi-dimensional array of binary columns int[ , , ,] x = prob.AddColumns(dim1 ,dim2 ,dim3 ,dim4) .WithType(Optimizer.Objects.ColumnType.Binary) .ToArray(); See Optimizer.VariableBuilder.ColumnArray4Builder for details of how to modify the specification in the builder.
	AddColumns(Int32, Int32, Int32, Int32, Int32)	Create an 5-dimensional array of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToArray() function. // Create a multi-dimensional array of binary columns int[ , , , ,] x = prob.AddColumns(dim1 ,dim2 ,dim3 ,dim4 ,dim5) .WithType(Optimizer.Objects.ColumnType.Binary) .ToArray(); See Optimizer.VariableBuilder.ColumnArray5Builder for details of how to modify the specification in the builder.
	AddColumns<K1>(ICollection<K1>)	Create an 1-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns System.Collections.Generic.Dictionary<K1 ,int> x = prob.AddColumns(coll1 ) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMapBuilder for details of how to modify the specification in the builder.
	AddColumns<K1>(IEnumerable<K1>)	Create an 1-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns System.Collections.Generic.Dictionary<K1 ,int> x = prob.AddColumns(coll1 ) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMapBuilder for details of how to modify the specification in the builder.
	AddColumns<K1>(K1)	Create an 1-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns System.Collections.Generic.Dictionary<K1 ,int> x = prob.AddColumns(coll1 ) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMapBuilder for details of how to modify the specification in the builder.
	AddColumns<K1, K2>(ICollection<K1>, ICollection<K2>)	Create an 2-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns Optimizer.Maps.HashMap2<K1 ,K2,int> x = prob.AddColumns(coll1 ,coll2) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMap2Builder for details of how to modify the specification in the builder.
	AddColumns<K1, K2>(K1, K2)	Create an 2-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns Optimizer.Maps.HashMap2<K1 ,K2,int> x = prob.AddColumns(coll1 ,coll2) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMap2Builder for details of how to modify the specification in the builder.
	AddColumns<K1, K2, K3>(ICollection<K1>, ICollection<K2>, ICollection<K3>)	Create an 3-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns Optimizer.Maps.HashMap3<K1 ,K2 ,K3,int> x = prob.AddColumns(coll1 ,coll2 ,coll3) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMap3Builder for details of how to modify the specification in the builder.
	AddColumns<K1, K2, K3>(K1, K2, K3)	Create an 3-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns Optimizer.Maps.HashMap3<K1 ,K2 ,K3,int> x = prob.AddColumns(coll1 ,coll2 ,coll3) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMap3Builder for details of how to modify the specification in the builder.
	AddColumns<K1, K2, K3, K4>(ICollection<K1>, ICollection<K2>, ICollection<K3>, ICollection<K4>)	Create an 4-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns Optimizer.Maps.HashMap4<K1 ,K2 ,K3 ,K4,int> x = prob.AddColumns(coll1 ,coll2 ,coll3 ,coll4) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMap4Builder for details of how to modify the specification in the builder.
	AddColumns<K1, K2, K3, K4>(K1, K2, K3, K4)	Create an 4-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns Optimizer.Maps.HashMap4<K1 ,K2 ,K3 ,K4,int> x = prob.AddColumns(coll1 ,coll2 ,coll3 ,coll4) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMap4Builder for details of how to modify the specification in the builder.
	AddColumns<K1, K2, K3, K4, K5>(ICollection<K1>, ICollection<K2>, ICollection<K3>, ICollection<K4>, ICollection<K5>)	Create an 5-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns Optimizer.Maps.HashMap5<K1 ,K2 ,K3 ,K4 ,K5,int> x = prob.AddColumns(coll1 ,coll2 ,coll3 ,coll4 ,coll5) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMap5Builder for details of how to modify the specification in the builder.
	AddColumns<K1, K2, K3, K4, K5>(K1, K2, K3, K4, K5)	Create an 5-dimensional map of columns. This function returns a builder that generates columns according to a specification. The specification can be modified. In order to actually create the columns and get their indices, you have to call the returned builder's ToMap() function. // Create a multi-dimensional array of binary columns Optimizer.Maps.HashMap5<K1 ,K2 ,K3 ,K4 ,K5,int> x = prob.AddColumns(coll1 ,coll2 ,coll3 ,coll4 ,coll5) .WithType(Optimizer.Objects.ColumnType.Binary) .ToMap(); See Optimizer.VariableBuilder.ColumnMap5Builder for details of how to modify the specification in the builder.
	AddComputeRestartCallback(ComputeRestartCallback)	Add ComputeRestart callback.
	AddComputeRestartCallback(ComputeRestartCallback, Int32)	Add ComputeRestart callback with the specified priority.
	AddComputeRestartCallback(ComputeRestartCallback, Object)	Add ComputeRestart callback with a user object that is passed to the callback on invocation.
	AddComputeRestartCallback(ComputeRestartCallback, Object, Int32)	Add ComputeRestart callback with specified priority and a user object that is passed to the callback on invocation.
	AddCut(Int32, XPRSprob.RowInfo)	Add a single cut to the problem.
	AddCut(Int32, Int32, Double, Char, Double)	Add a single cut to the problem.
	AddCutlogCallback(CutlogCallback)	Add Cutlog callback.
	AddCutlogCallback(CutlogCallback, Int32)	Add Cutlog callback with the specified priority.
	AddCutlogCallback(CutlogCallback, Object)	Add Cutlog callback with a user object that is passed to the callback on invocation.
	AddCutlogCallback(CutlogCallback, Object, Int32)	Add Cutlog callback with specified priority and a user object that is passed to the callback on invocation.
	AddCutmgrCallback(CutmgrCallback)	Obsolete. Add Cutmgr callback.
	AddCutmgrCallback(CutmgrCallback, Int32)	Obsolete. Add Cutmgr callback with the specified priority.
	AddCutmgrCallback(CutmgrCallback, Object)	Obsolete. Add Cutmgr callback with a user object that is passed to the callback on invocation.
	AddCutmgrCallback(CutmgrCallback, Object, Int32)	Obsolete. Add Cutmgr callback with specified priority and a user object that is passed to the callback on invocation.
	AddCuts(Int32, Int32, Char, Double, Int32, Int32, Double)	Adds cuts directly to the matrix at the current node. The cuts will automatically be added to the cut pool. Cuts added to a node will automatically be inherited on any descendant node, unless explicitly deleted with a call to delCuts.
	AddCuts(Int32, Int32, Char, Double, Int64, Int32, Double)	Adds cuts directly to the matrix at the current node. The cuts will automatically be added to the cut pool. Cuts added to a node will automatically be inherited on any descendant node, unless explicitly deleted with a call to delCuts.
	AddGapNotifyCallback(GapNotifyCallback)	Add GapNotify callback.
	AddGapNotifyCallback(GapNotifyCallback, Int32)	Add GapNotify callback with the specified priority.
	AddGapNotifyCallback(GapNotifyCallback, Object)	Add GapNotify callback with a user object that is passed to the callback on invocation.
	AddGapNotifyCallback(GapNotifyCallback, Object, Int32)	Add GapNotify callback with specified priority and a user object that is passed to the callback on invocation.
	AddGenCons(Int32, Int32, Int32, GenConsType, Int32, Int32, Int32, Int32, Double)	Adds one or more general constraints to the problem. Each general constraint y = f(x1, ..., xn, c1, ..., cn) consists of one or more (input) columns xi, zero or more constant values ci and a resultant (output column) y, different from all xi. General constraints include maximum and minimum (arbitrary number of input columns of any type and arbitrary number of input values, at least one total), and and or (at least one binary input column, no constant values, binary resultant) and absolute value (exactly one input column of arbitrary type, no constant values).
	AddGenCons(Int32, Int64, Int64, GenConsType, Int32, Int64, Int32, Int64, Double)	Adds one or more general constraints to the problem. Each general constraint y = f(x1, ..., xn, c1, ..., cn) consists of one or more (input) columns xi, zero or more constant values ci and a resultant (output column) y, different from all xi. General constraints include maximum and minimum (arbitrary number of input columns of any type and arbitrary number of input values, at least one total), and and or (at least one binary input column, no constant values, binary resultant) and absolute value (exactly one input column of arbitrary type, no constant values).
	AddInfnodeCallback(InfnodeCallback)	Add Infnode callback.
	AddInfnodeCallback(InfnodeCallback, Int32)	Add Infnode callback with the specified priority.
	AddInfnodeCallback(InfnodeCallback, Object)	Add Infnode callback with a user object that is passed to the callback on invocation.
	AddInfnodeCallback(InfnodeCallback, Object, Int32)	Add Infnode callback with specified priority and a user object that is passed to the callback on invocation.
	AddIntsolCallback(IntsolCallback)	Add Intsol callback.
	AddIntsolCallback(IntsolCallback, Int32)	Add Intsol callback with the specified priority.
	AddIntsolCallback(IntsolCallback, Object)	Add Intsol callback with a user object that is passed to the callback on invocation.
	AddIntsolCallback(IntsolCallback, Object, Int32)	Add Intsol callback with specified priority and a user object that is passed to the callback on invocation.
	AddLplogCallback(LplogCallback)	Add Lplog callback.
	AddLplogCallback(LplogCallback, Int32)	Add Lplog callback with the specified priority.
	AddLplogCallback(LplogCallback, Object)	Add Lplog callback with a user object that is passed to the callback on invocation.
	AddLplogCallback(LplogCallback, Object, Int32)	Add Lplog callback with specified priority and a user object that is passed to the callback on invocation.
	AddMessageCallback(MessageCallback)	Add Message callback.
	AddMessageCallback(MessageCallback, Int32)	Add Message callback with the specified priority.
	AddMessageCallback(MessageCallback, Object)	Add Message callback with a user object that is passed to the callback on invocation.
	AddMessageCallback(MessageCallback, Object, Int32)	Add Message callback with specified priority and a user object that is passed to the callback on invocation.
	AddMiplogCallback(MiplogCallback)	Add Miplog callback.
	AddMiplogCallback(MiplogCallback, Int32)	Add Miplog callback with the specified priority.
	AddMiplogCallback(MiplogCallback, Object)	Add Miplog callback with a user object that is passed to the callback on invocation.
	AddMiplogCallback(MiplogCallback, Object, Int32)	Add Miplog callback with specified priority and a user object that is passed to the callback on invocation.
	AddMipSol(Double, Int32)	Add a new MIP solution. This is a convenience wrapper for .AddMipSol(int, double[], int[], string ).
	AddMipSol(Double, Int32, String)	Add a new MIP solution. This is a convenience wrapper for .AddMipSol(int, double[], int[], string ).
	AddMipSol(Int32, Double, Int32, String)	Adds a new feasible, infeasible or partial MIP solution for the problem to the Optimizer.
	AddMipThreadCallback(MipThreadCallback)	Add MipThread callback.
	AddMipThreadCallback(MipThreadCallback, Int32)	Add MipThread callback with the specified priority.
	AddMipThreadCallback(MipThreadCallback, Object)	Add MipThread callback with a user object that is passed to the callback on invocation.
	AddMipThreadCallback(MipThreadCallback, Object, Int32)	Add MipThread callback with specified priority and a user object that is passed to the callback on invocation.
	AddMipThreadDestroyCallback(MipThreadDestroyCallback)	Add MipThreadDestroy callback.
	AddMipThreadDestroyCallback(MipThreadDestroyCallback, Int32)	Add MipThreadDestroy callback with the specified priority.
	AddMipThreadDestroyCallback(MipThreadDestroyCallback, Object)	Add MipThreadDestroy callback with a user object that is passed to the callback on invocation.
	AddMipThreadDestroyCallback(MipThreadDestroyCallback, Object, Int32)	Add MipThreadDestroy callback with specified priority and a user object that is passed to the callback on invocation.
	AddMsgHandlerCallback(TextWriter)	(Inherited from XPRSobject.)
	AddMsgHandlerCallback(MsgHandlerCallback)	Add MsgHandler callback. (Overrides XPRSobject.AddMsgHandlerCallback(MsgHandlerCallback).)
	AddMsgHandlerCallback(MsgHandlerCallback, Int32)	Add MsgHandler callback with the specified priority. (Overrides XPRSobject.AddMsgHandlerCallback(MsgHandlerCallback, Int32).)
	AddMsgHandlerCallback(MsgHandlerCallback, Object)	Add MsgHandler callback with a user object that is passed to the callback on invocation. (Overrides XPRSobject.AddMsgHandlerCallback(MsgHandlerCallback, Object).)
	AddMsgHandlerCallback(MsgHandlerCallback, Object, Int32)	Add MsgHandler callback with specified priority and a user object that is passed to the callback on invocation. (Overrides XPRSobject.AddMsgHandlerCallback(MsgHandlerCallback, Object, Int32).)
	AddMsJobEndCallback(MsJobEndCallback)	Add MsJobEnd callback.
	AddMsJobEndCallback(MsJobEndCallback, Int32)	Add MsJobEnd callback with the specified priority.
	AddMsJobEndCallback(MsJobEndCallback, Object)	Add MsJobEnd callback with a user object that is passed to the callback on invocation.
	AddMsJobEndCallback(MsJobEndCallback, Object, Int32)	Add MsJobEnd callback with specified priority and a user object that is passed to the callback on invocation.
	AddMsJobStartCallback(MsJobStartCallback)	Add MsJobStart callback.
	AddMsJobStartCallback(MsJobStartCallback, Int32)	Add MsJobStart callback with the specified priority.
	AddMsJobStartCallback(MsJobStartCallback, Object)	Add MsJobStart callback with a user object that is passed to the callback on invocation.
	AddMsJobStartCallback(MsJobStartCallback, Object, Int32)	Add MsJobStart callback with specified priority and a user object that is passed to the callback on invocation.
	AddMsWinnerCallback(MsWinnerCallback)	Add MsWinner callback.
	AddMsWinnerCallback(MsWinnerCallback, Int32)	Add MsWinner callback with the specified priority.
	AddMsWinnerCallback(MsWinnerCallback, Object)	Add MsWinner callback with a user object that is passed to the callback on invocation.
	AddMsWinnerCallback(MsWinnerCallback, Object, Int32)	Add MsWinner callback with specified priority and a user object that is passed to the callback on invocation.
	AddNames(Int32, String, Int32, Int32)	Add names of the given type to the problem.
	AddNames(Namespaces, String, Int32, Int32)	Add names to model. Note that if this method fails, then it is unspecified how many of the names were changed.
	AddNewnodeCallback(NewnodeCallback)	Add Newnode callback.
	AddNewnodeCallback(NewnodeCallback, Int32)	Add Newnode callback with the specified priority.
	AddNewnodeCallback(NewnodeCallback, Object)	Add Newnode callback with a user object that is passed to the callback on invocation.
	AddNewnodeCallback(NewnodeCallback, Object, Int32)	Add Newnode callback with specified priority and a user object that is passed to the callback on invocation.
	AddNlpCoefEvalErrorCallback(NlpCoefEvalErrorCallback)	Add NlpCoefEvalError callback.
	AddNlpCoefEvalErrorCallback(NlpCoefEvalErrorCallback, Int32)	Add NlpCoefEvalError callback with the specified priority.
	AddNlpCoefEvalErrorCallback(NlpCoefEvalErrorCallback, Object)	Add NlpCoefEvalError callback with a user object that is passed to the callback on invocation.
	AddNlpCoefEvalErrorCallback(NlpCoefEvalErrorCallback, Object, Int32)	Add NlpCoefEvalError callback with specified priority and a user object that is passed to the callback on invocation.
	AddNodecutoffCallback(NodecutoffCallback)	Add Nodecutoff callback.
	AddNodecutoffCallback(NodecutoffCallback, Int32)	Add Nodecutoff callback with the specified priority.
	AddNodecutoffCallback(NodecutoffCallback, Object)	Add Nodecutoff callback with a user object that is passed to the callback on invocation.
	AddNodecutoffCallback(NodecutoffCallback, Object, Int32)	Add Nodecutoff callback with specified priority and a user object that is passed to the callback on invocation.
	AddNodeLPSolvedCallback(NodeLPSolvedCallback)	Add NodeLPSolved callback.
	AddNodeLPSolvedCallback(NodeLPSolvedCallback, Int32)	Add NodeLPSolved callback with the specified priority.
	AddNodeLPSolvedCallback(NodeLPSolvedCallback, Object)	Add NodeLPSolved callback with a user object that is passed to the callback on invocation.
	AddNodeLPSolvedCallback(NodeLPSolvedCallback, Object, Int32)	Add NodeLPSolved callback with specified priority and a user object that is passed to the callback on invocation.
	AddObj	Appends an objective function with the given coefficients to a multi-objective problem. The weight and priority of the objective are set to the given values.
	AddOptnodeCallback(OptnodeCallback)	Add Optnode callback.
	AddOptnodeCallback(OptnodeCallback, Int32)	Add Optnode callback with the specified priority.
	AddOptnodeCallback(OptnodeCallback, Object)	Add Optnode callback with a user object that is passed to the callback on invocation.
	AddOptnodeCallback(OptnodeCallback, Object, Int32)	Add Optnode callback with specified priority and a user object that is passed to the callback on invocation.
	AddPreIntsolCallback(PreIntsolCallback)	Add PreIntsol callback.
	AddPreIntsolCallback(PreIntsolCallback, Int32)	Add PreIntsol callback with the specified priority.
	AddPreIntsolCallback(PreIntsolCallback, Object)	Add PreIntsol callback with a user object that is passed to the callback on invocation.
	AddPreIntsolCallback(PreIntsolCallback, Object, Int32)	Add PreIntsol callback with specified priority and a user object that is passed to the callback on invocation.
	AddPrenodeCallback(PrenodeCallback)	Add Prenode callback.
	AddPrenodeCallback(PrenodeCallback, Int32)	Add Prenode callback with the specified priority.
	AddPrenodeCallback(PrenodeCallback, Object)	Add Prenode callback with a user object that is passed to the callback on invocation.
	AddPrenodeCallback(PrenodeCallback, Object, Int32)	Add Prenode callback with specified priority and a user object that is passed to the callback on invocation.
	AddPresolveCallback(PresolveCallback)	Add Presolve callback.
	AddPresolveCallback(PresolveCallback, Int32)	Add Presolve callback with the specified priority.
	AddPresolveCallback(PresolveCallback, Object)	Add Presolve callback with a user object that is passed to the callback on invocation.
	AddPresolveCallback(PresolveCallback, Object, Int32)	Add Presolve callback with specified priority and a user object that is passed to the callback on invocation.
	AddPwlCons(Int32, Int32, Int32, Int32, Int32, Double, Double)	Adds one or more piecewise linear constraints to the problem. Each piecewise linear constraint y = f(x) consists of an (input) column x, a (different) resultant (output column) y and a piecewise linear function f. The piecewise linear function f is described by at least two breakpoints, which are given as combinations of x- and y-values. Discontinuous piecewise linear functions are supported, in this case both the left and right limit at a given point need to be entered as breakpoints. To differentiate between left and right limit, the breakpoints need to be given as a list with non-decreasing x-values.
	AddPwlCons(Int32, Int64, Int32, Int32, Int64, Double, Double)	Adds one or more piecewise linear constraints to the problem. Each piecewise linear constraint y = f(x) consists of an (input) column x, a (different) resultant (output column) y and a piecewise linear function f. The piecewise linear function f is described by at least two breakpoints, which are given as combinations of x- and y-values. Discontinuous piecewise linear functions are supported, in this case both the left and right limit at a given point need to be entered as breakpoints. To differentiate between left and right limit, the breakpoints need to be given as a list with non-decreasing x-values.
	AddQMatrix(Int32, Int32, Int32, Int32, Double)	Adds a new quadratic matrix into a row defined by triplets.
	AddQMatrix(Int32, Int64, Int32, Int32, Double)	Adds a new quadratic matrix into a row defined by triplets.
	AddRow(XPRSprob.RowInfo)	Add a single row to the problem.
	AddRow(XPRSprob.RowInfo, String)	Add a single row to the problem.
	AddRow(Int32, Double, Char, Double)	Add a single row to the problem. This is a short cut for AddRow(colind, colval, rowtype, rhs, null, null).
	AddRow(Int32, Double, Char, Double, String)	Add a single row to the problem.
	AddRow(Int32, Double, Char, Double, Double, String)	Add a single row to the problem.
	AddRows(Int32, Int32, Char, Double, Int32, Int32, Double)	Adds rows to the optimizer matrix.
	AddRows(Int32, Int64, Char, Double, Int64, Int32, Double)	Adds rows to the optimizer matrix.
	AddRows(Int32, Int32, Char, Double, Double, Int32, Int32, Double)	Adds rows to the optimizer matrix.
	AddRows(Int32, Int64, Char, Double, Double, Int64, Int32, Double)	Adds rows to the optimizer matrix.
	AddSet	Add a single set constraint to this problem.
	AddSetNames	Add the given set names to the problem.
	AddSets(SetType, Int32, Double, String)	Add multiple set constraints to the problem.
	AddSets(Int32, Int32, Char, Int32, Int32, Double)	Allows sets to be added to the problem after passing it to the Optimizer using the input routines.
	AddSets(Int32, Int32, SetType, Int32, Double, String)	Create multiple set constraints.
	AddSets(Int32, Int64, Char, Int64, Int32, Double)	Allows sets to be added to the problem after passing it to the Optimizer using the input routines.
	AddSlpCascadeEndCallback(SlpCascadeEndCallback)	Add SlpCascadeEnd callback.
	AddSlpCascadeEndCallback(SlpCascadeEndCallback, Int32)	Add SlpCascadeEnd callback with the specified priority.
	AddSlpCascadeEndCallback(SlpCascadeEndCallback, Object)	Add SlpCascadeEnd callback with a user object that is passed to the callback on invocation.
	AddSlpCascadeEndCallback(SlpCascadeEndCallback, Object, Int32)	Add SlpCascadeEnd callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpCascadeStartCallback(SlpCascadeStartCallback)	Add SlpCascadeStart callback.
	AddSlpCascadeStartCallback(SlpCascadeStartCallback, Int32)	Add SlpCascadeStart callback with the specified priority.
	AddSlpCascadeStartCallback(SlpCascadeStartCallback, Object)	Add SlpCascadeStart callback with a user object that is passed to the callback on invocation.
	AddSlpCascadeStartCallback(SlpCascadeStartCallback, Object, Int32)	Add SlpCascadeStart callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpCascadeVarCallback(SlpCascadeVarCallback)	Add SlpCascadeVar callback.
	AddSlpCascadeVarCallback(SlpCascadeVarCallback, Int32)	Add SlpCascadeVar callback with the specified priority.
	AddSlpCascadeVarCallback(SlpCascadeVarCallback, Object)	Add SlpCascadeVar callback with a user object that is passed to the callback on invocation.
	AddSlpCascadeVarCallback(SlpCascadeVarCallback, Object, Int32)	Add SlpCascadeVar callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpCascadeVarFailCallback(SlpCascadeVarFailCallback)	Add SlpCascadeVarFail callback.
	AddSlpCascadeVarFailCallback(SlpCascadeVarFailCallback, Int32)	Add SlpCascadeVarFail callback with the specified priority.
	AddSlpCascadeVarFailCallback(SlpCascadeVarFailCallback, Object)	Add SlpCascadeVarFail callback with a user object that is passed to the callback on invocation.
	AddSlpCascadeVarFailCallback(SlpCascadeVarFailCallback, Object, Int32)	Add SlpCascadeVarFail callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpConstructCallback(SlpConstructCallback)	Add SlpConstruct callback.
	AddSlpConstructCallback(SlpConstructCallback, Int32)	Add SlpConstruct callback with the specified priority.
	AddSlpConstructCallback(SlpConstructCallback, Object)	Add SlpConstruct callback with a user object that is passed to the callback on invocation.
	AddSlpConstructCallback(SlpConstructCallback, Object, Int32)	Add SlpConstruct callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpDrColCallback(SlpDrColCallback)	Add SlpDrCol callback.
	AddSlpDrColCallback(SlpDrColCallback, Int32)	Add SlpDrCol callback with the specified priority.
	AddSlpDrColCallback(SlpDrColCallback, Object)	Add SlpDrCol callback with a user object that is passed to the callback on invocation.
	AddSlpDrColCallback(SlpDrColCallback, Object, Int32)	Add SlpDrCol callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpIntSolCallback(SlpIntSolCallback)	Add SlpIntSol callback.
	AddSlpIntSolCallback(SlpIntSolCallback, Int32)	Add SlpIntSol callback with the specified priority.
	AddSlpIntSolCallback(SlpIntSolCallback, Object)	Add SlpIntSol callback with a user object that is passed to the callback on invocation.
	AddSlpIntSolCallback(SlpIntSolCallback, Object, Int32)	Add SlpIntSol callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpIterEndCallback(SlpIterEndCallback)	Add SlpIterEnd callback.
	AddSlpIterEndCallback(SlpIterEndCallback, Int32)	Add SlpIterEnd callback with the specified priority.
	AddSlpIterEndCallback(SlpIterEndCallback, Object)	Add SlpIterEnd callback with a user object that is passed to the callback on invocation.
	AddSlpIterEndCallback(SlpIterEndCallback, Object, Int32)	Add SlpIterEnd callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpIterStartCallback(SlpIterStartCallback)	Add SlpIterStart callback.
	AddSlpIterStartCallback(SlpIterStartCallback, Int32)	Add SlpIterStart callback with the specified priority.
	AddSlpIterStartCallback(SlpIterStartCallback, Object)	Add SlpIterStart callback with a user object that is passed to the callback on invocation.
	AddSlpIterStartCallback(SlpIterStartCallback, Object, Int32)	Add SlpIterStart callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpIterVarCallback(SlpIterVarCallback)	Add SlpIterVar callback.
	AddSlpIterVarCallback(SlpIterVarCallback, Int32)	Add SlpIterVar callback with the specified priority.
	AddSlpIterVarCallback(SlpIterVarCallback, Object)	Add SlpIterVar callback with a user object that is passed to the callback on invocation.
	AddSlpIterVarCallback(SlpIterVarCallback, Object, Int32)	Add SlpIterVar callback with specified priority and a user object that is passed to the callback on invocation.
	AddSlpPreUpdateLinearizationCallback(SlpPreUpdateLinearizationCallback)	Add SlpPreUpdateLinearization callback.
	AddSlpPreUpdateLinearizationCallback(SlpPreUpdateLinearizationCallback, Int32)	Add SlpPreUpdateLinearization callback with the specified priority.
	AddSlpPreUpdateLinearizationCallback(SlpPreUpdateLinearizationCallback, Object)	Add SlpPreUpdateLinearization callback with a user object that is passed to the callback on invocation.
	AddSlpPreUpdateLinearizationCallback(SlpPreUpdateLinearizationCallback, Object, Int32)	Add SlpPreUpdateLinearization callback with specified priority and a user object that is passed to the callback on invocation.
	AddUserSolNotifyCallback(UserSolNotifyCallback)	Add UserSolNotify callback.
	AddUserSolNotifyCallback(UserSolNotifyCallback, Int32)	Add UserSolNotify callback with the specified priority.
	AddUserSolNotifyCallback(UserSolNotifyCallback, Object)	Add UserSolNotify callback with a user object that is passed to the callback on invocation.
	AddUserSolNotifyCallback(UserSolNotifyCallback, Object, Int32)	Add UserSolNotify callback with specified priority and a user object that is passed to the callback on invocation.
	Alter	Alters or changes matrix elements, right hand sides and constraint senses in the current problem.
	BasisCondition	Obsolete. Calculates the condition number of the current basis after solving the LP relaxation.
	BasisStability(Int32, Int32, Int32)	Convenience wrapper for BasisStability(int,int,int,double) that returns the output argument.
	BasisStability(Int32, Int32, Int32, Double)	Calculates various measures for the stability of the current basis, including the basis condition number.
	BinVar()	Create a new binary variable.
	BinVar(String)	Create a new binary variable with the specified name.
	BinVarArray(Int32, Func<Int32, String>)	Create an array of binary variables.
	BinVarArray<T>(ICollection<T>, Func<T, String>)	Create an array of binary variables. The function will create one variable for each of the objects listed in objs.
	BinVarMap<T>(ICollection<T>, Func<T, String>)	Create a map of binary variables. The function creates a new variable for each object in objs. It returns a hash map in which each object in objs maps to the index of the variable that was created for it.
	BinVarMap<T>(ICollection<T>, Func<T, String>, IDictionary<T, Int32>)	Create a map of binary variables. The function creates a new variable for each object in objs. For each object o it puts the Pair (o, idx) into map where idx is the index of the variable that was created for o.
	Bndsa	Returns upper and lower sensitivity ranges for specified variables' lower and upper bounds. If the bounds are varied within these ranges the current basis remains optimal and feasible.
	BTran	Post-multiplies a (row) vector provided by the user by the inverse of the current basis.
	BuildColumns(VariableBuilder.Array2Builder)	Add variables as specified by the builder.
	BuildColumns(VariableBuilder.Array3Builder)	Add variables as specified by the builder.
	BuildColumns(VariableBuilder.Array4Builder)	Add variables as specified by the builder.
	BuildColumns(VariableBuilder.Array5Builder)	Add variables as specified by the builder.
	BuildColumns(VariableBuilder.ArrayBuilder)	Add variables as specified by the builder.
	BuildColumns(VariableBuilder.Array2Builder, Action<Int32>)	TODO: Hide this from the user.
	BuildColumns(VariableBuilder.Array3Builder, Action<Int32>)	TODO: Hide this from the user.
	BuildColumns(VariableBuilder.Array4Builder, Action<Int32>)	TODO: Hide this from the user.
	BuildColumns(VariableBuilder.Array5Builder, Action<Int32>)	TODO: Hide this from the user.
	BuildColumns(VariableBuilder.ArrayBuilder, Action<Int32>)	TODO: Hide this from the user.
	BuildColumns<K1>(VariableBuilder.MapBuilder<K1>)	Create a column map from a builder.
	BuildColumns<I>(VariableBuilder.Array2Builder, Func<I>, Action<I, Int32, Int32, Int32>)	Add columns as specified by the builder. This is a parametrized version of .BuildColumns(VariableBuilder.Array2Builder).
	BuildColumns<I>(VariableBuilder.Array3Builder, Func<I>, Action5<I, Int32, Int32, Int32, Int32>)	Add columns as specified by the builder. This is a parametrized version of .BuildColumns(VariableBuilder.Array3Builder).
	BuildColumns<I>(VariableBuilder.Array4Builder, Func<I>, Action6<I, Int32, Int32, Int32, Int32, Int32>)	Add columns as specified by the builder. This is a parametrized version of .BuildColumns(VariableBuilder.Array4Builder).
	BuildColumns<I>(VariableBuilder.Array5Builder, Func<I>, Action7<I, Int32, Int32, Int32, Int32, Int32, Int32>)	Add columns as specified by the builder. This is a parametrized version of .BuildColumns(VariableBuilder.Array5Builder).
	BuildColumns<I>(VariableBuilder.ArrayBuilder, Func<I>, Action<I, Int32, Int32>)	Add columns as specified by the builder. This is a parametrized version of .BuildColumns(VariableBuilder.ArrayBuilder).
	BuildColumns<K1>(VariableBuilder.MapBuilder<K1>, Action<K1, Int32>, Action)	Create a column map from a builder. This function parametrizes creation of a map that maps keys to created objects.
	BuildColumns<K1, K2>(VariableBuilder.Map2Builder<K1, K2>)	Create a column map from a builder.
	BuildColumns<K1, K2>(VariableBuilder.Map2Builder<K1, K2>, Action<K1, K2, Int32>, Action)	Create a column map from a builder. This function parametrizes creation of a map that maps keys to created objects.
	BuildColumns<I, K1>(VariableBuilder.MapBuilder<K1>, Func<I>, Action<I, K1, Int32>)	Create a column map from a builder.
	BuildColumns<K1, K2, K3>(VariableBuilder.Map3Builder<K1, K2, K3>)	Create a column map from a builder.
	BuildColumns<I, K1, K2>(VariableBuilder.Map2Builder<K1, K2>, Func<I>, Action<I, K1, K2, Int32>)	Create a column map from a builder.
	BuildColumns<K1, K2, K3>(VariableBuilder.Map3Builder<K1, K2, K3>, Action<K1, K2, K3, Int32>, Action)	Create a column map from a builder. This function parametrizes creation of a map that maps keys to created objects.
	BuildColumns<K1, K2, K3, K4>(VariableBuilder.Map4Builder<K1, K2, K3, K4>)	Create a column map from a builder.
	BuildColumns<I, K1, K2, K3>(VariableBuilder.Map3Builder<K1, K2, K3>, Func<I>, Action5<I, K1, K2, K3, Int32>)	Create a column map from a builder.
	BuildColumns<K1, K2, K3, K4>(VariableBuilder.Map4Builder<K1, K2, K3, K4>, Action5<K1, K2, K3, K4, Int32>, Action)	Create a column map from a builder. This function parametrizes creation of a map that maps keys to created objects.
	BuildColumns<K1, K2, K3, K4, K5>(VariableBuilder.Map5Builder<K1, K2, K3, K4, K5>)	Create a column map from a builder.
	BuildColumns<I, K1, K2, K3, K4>(VariableBuilder.Map4Builder<K1, K2, K3, K4>, Func<I>, Action6<I, K1, K2, K3, K4, Int32>)	Create a column map from a builder.
	BuildColumns<K1, K2, K3, K4, K5>(VariableBuilder.Map5Builder<K1, K2, K3, K4, K5>, Action6<K1, K2, K3, K4, K5, Int32>, Action)	Create a column map from a builder. This function parametrizes creation of a map that maps keys to created objects.
	BuildColumns<I, K1, K2, K3, K4, K5>(VariableBuilder.Map5Builder<K1, K2, K3, K4, K5>, Func<I>, Action7<I, K1, K2, K3, K4, K5, Int32>)	Create a column map from a builder.
	CalcObjective(Double)	Convenience wrapper for CalcObjective(double[],double) that returns the output argument.
	CalcObjective(Double, Double)	Calculates the objective value of a given solution.
	CalcObjN(Int32, Double)	Convenience wrapper for CalcObjN(int,double[],double) that returns the output argument.
	CalcObjN(Int32, Double, Double)	Calculates the objective value of the given objective function in a multi-objective problem.
	CalcReducedCosts	Calculates the reduced cost values for a given (row) dual solution.
	CalcSlacks	Calculates the row slack values for a given solution.
	CalcSolInfo(Double, Double, Int32)	Convenience wrapper for CalcSolInfo(double[],double[],int,double) that returns the output argument.
	CalcSolInfo(Double, Double, Int32, Double)	Calculates the required property of a solution, like maximum infeasibility of a given primal and dual solution.
	ChgBounds(Int32, Double, Double)	Change bounds of a single column.
	ChgBounds(Int32, Int32, Char, Double)	Used to change the bounds on columns in the matrix.
	ChgCoef	Used to change a single coefficient in the matrix. If the coefficient does not already exist, a new coefficient will be added to the matrix. If many coefficients are being added to a row of the matrix, it may be more efficient to delete the old row of the matrix and add a new row.
	ChgColType(Int32, Char)	Convenience wrapper for ChgColType(int, int[], char[]).
	ChgColType(Int32, Int32, Char)	Used to change the type of a specified set of columns in the matrix.
	ChgGlbLimit(Int32, Double)	Convenience wrapper for ChgGlbLimit(int, int[], double[]).
	ChgGlbLimit(Int32, Int32, Double)	Used to change semi-continuous or semi-integer lower bounds, or upper limits on partial integers.
	ChgLB	Change the lower bound of a single column.
	ChgMCoef(Int32, Int32, Int32, Double)	Used to change multiple coefficients in the matrix. If any coefficient does not already exist, it will be added to the matrix. If many coefficients are being added to a row of the matrix, it may be more efficient to delete the old row of the matrix and add a new one.
	ChgMCoef(Int64, Int32, Int32, Double)	Used to change multiple coefficients in the matrix. If any coefficient does not already exist, it will be added to the matrix. If many coefficients are being added to a row of the matrix, it may be more efficient to delete the old row of the matrix and add a new one.
	ChgMQObj(Int32, Int32, Int32, Double)	Used to change multiple quadratic coefficients in the objective function. If any of the coefficients does not exist already, new coefficients will be added to the objective function.
	ChgMQObj(Int64, Int32, Int32, Double)	Used to change multiple quadratic coefficients in the objective function. If any of the coefficients does not exist already, new coefficients will be added to the objective function.
	ChgObj(Int32, Double)	Convenience wrapper for ChgObj(int, int[], double[]).
	ChgObj(Int32, Int32, Double)	Used to change the objective function coefficients.
	ChgObjN	Modifies one or more coefficients of an objective function in a multi-objective problem. If the objective already exists, any coefficients not present in the colind and objcoef arrays will unchanged. If the objective does not exist, it will be added to the problem.
	ChgObjSense(Int32)
	ChgObjSense(ObjSense)	Changes the problem's objective function sense to minimize or maximize.
	ChgQObj	Used to change a single quadratic coefficient in the objective function corresponding to the variable pair (objqcol1,objqcol2) of the Hessian matrix.
	ChgQRowCoeff	Changes a single quadratic coefficient in a row.
	ChgRHS(Int32, Double)	Convenience wrapper for ChgRHS(int, int[], double[]).
	ChgRHS(Int32, Int32, Double)	Used to change right--hand side values of the problem.
	ChgRHSRange(Int32, Double)	Convenience wrapper for ChgRHSRange(int, int[], double[]).
	ChgRHSRange(Int32, Int32, Double)	Used to change the range for a row of the problem matrix.
	ChgRowType(Int32, Char)	Convenience wrapper for ChgRowType(int, int[], char[]).
	ChgRowType(Int32, Int32, Char)	Used to change the type of a row in the matrix.
	ChgUB	Change the upper bound of a single column.
	ClearIIS	Resets the search for Irreducible Infeasible Sets (IIS).
	ClearObjective	Clear the objective function.
	ClearRowFlags	Clears extra information attached to a range of rows.
	Clone	Create a copy of the problem, including controls and callbacks.
	ColumnTypeToArray	Convert a column type array to an array of low-level type indicators.
	ContVar()	Create a continuous variable with default bounds [0, infinity].
	ContVar(String)	Create a continuous variable with default bounds [0, infinity] and a specified name.
	ContVar(Double, Double)	Create a continuous variable with specified bounds.
	ContVar(Double, Double, String)	Create a continuous variable with specified bounds and name.
	ContVarArray(Int32, Double, Double, Func<Int32, String>)	Create an array of continuous variables. All the variables created by this function have the same types and bounds.
	ContVarArray(Int32, Double, Double, String)	Create an array of continuous variables.
	ContVarArray(Int32, Func<Int32, Double>, Func<Int32, Double>, Func<Int32, String>)	Create an array of continuous variables.
	ContVarArray<T>(ICollection<T>, Double, Double, Func<T, String>)	Create an array of continuous variables. All the variables created by this function have the same types and bounds. The function will create one variable for each of the objects listed in objs.
	ContVarArray<T>(ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>)	Create an array of continuous variables. The function will create one variable for each of the objects listed in objs.
	ContVarMap<T>(ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>)	Create a map of continuous variables. The function creates a new variable for each object in objs. It returns a hash map in which each object in objs maps to the index of the variable that was created for it.
	ContVarMap<T>(ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>, IDictionary<T, Int32>)	Create a map of continuous variables. The function creates a new variable for each object in objs. For each object o it puts the Pair (o, idx) into map where idx is the index of the variable that was created for o.
	CopyCallbacks	Copy callbacks to this XPRSprob from another
	CopyControls	Copies controls defined for one problem to another.
	CopyProb(XPRSprob)	Create a copy of the problem.
	CopyProb(XPRSprob, String)	Create a copy of the problem with the given name.
	CreateBranchObject	Create a branching object.
	CreateBranchObjectFromGlobal	Obsolete.
	CrossoverLpSol()	Convenience wrapper for CrossoverLpSol(int) that returns the output argument.
	CrossoverLpSol(Int32)	Provides a basic optimal solution for a given solution of an LP problem. This function behaves like the crossover after the barrier algorithm.
	DelCols	Delete columns from a matrix.
	DelCPCuts()	During the branch and bound search, cuts are stored in the cut pool to be applied at descendant nodes. These cuts may be removed from a given node using delCuts, but if this is to be applied in a large number of cases, it may be preferable to remove the cut completely from the cut pool. This is achieved using delCPCuts.
	DelCPCuts(Int32, Cut)	During the branch and bound search, cuts are stored in the cut pool to be applied at descendant nodes. These cuts may be removed from a given node using delCuts, but if this is to be applied in a large number of cases, it may be preferable to remove the cut completely from the cut pool. This is achieved using delCPCuts.
	DelCPCuts(Int32, Int32)	During the branch and bound search, cuts are stored in the cut pool to be applied at descendant nodes. These cuts may be removed from a given node using delCuts, but if this is to be applied in a large number of cases, it may be preferable to remove the cut completely from the cut pool. This is achieved using delCPCuts.
	DelCPCuts(Int32, Int32, Int32, Cut)	During the branch and bound search, cuts are stored in the cut pool to be applied at descendant nodes. These cuts may be removed from a given node using delCuts, but if this is to be applied in a large number of cases, it may be preferable to remove the cut completely from the cut pool. This is achieved using delCPCuts.
	DelCuts(Int32)	Deletes cuts from the matrix at the current node. Cuts from the parent node which have been automatically restored may be deleted as well as cuts added to the current node using addCuts or loadCuts. The cuts to be deleted can be specified in a number of ways. If a cut is ruled out by any one of the criteria it will not be deleted.
	DelCuts(Int32, Int32, Cut)	Deletes cuts from the matrix at the current node. Cuts from the parent node which have been automatically restored may be deleted as well as cuts added to the current node using addCuts or loadCuts. The cuts to be deleted can be specified in a number of ways. If a cut is ruled out by any one of the criteria it will not be deleted.
	DelCuts(Int32, Int32, Int32)	Deletes cuts from the matrix at the current node. Cuts from the parent node which have been automatically restored may be deleted as well as cuts added to the current node using addCuts or loadCuts. The cuts to be deleted can be specified in a number of ways. If a cut is ruled out by any one of the criteria it will not be deleted.
	DelCuts(Int32, Int32, Int32, Double)	Deletes cuts from the matrix at the current node. Cuts from the parent node which have been automatically restored may be deleted as well as cuts added to the current node using addCuts or loadCuts. The cuts to be deleted can be specified in a number of ways. If a cut is ruled out by any one of the criteria it will not be deleted.
	DelCuts(Int32, Int32, Int32, Double, Int32, Cut)	Deletes cuts from the matrix at the current node. Cuts from the parent node which have been automatically restored may be deleted as well as cuts added to the current node using addCuts or loadCuts. The cuts to be deleted can be specified in a number of ways. If a cut is ruled out by any one of the criteria it will not be deleted.
	DelGenCons	Delete general constraints from a problem.
	DelIndicator	Delete a single indicator constraint. This only deletes the "indicator property from the specified row. Neither the associated variable nor the row are deleted.
	DelIndicators	Delete indicator constraints. This turns the specified rows into normal rows (not controlled by indicator variables).
	DelObj	Removes an objective function from a multi-objective problem. Any objectives with index > objidx will be shifted down. Deleting the last objective function in the problem causes all the objective coefficients to be zeroed, but OBJECTIVES remains set to 1.
	DelPwlCons	Delete piecewise linear constraints from a problem.
	DelQMatrix	Deletes the quadratic part of a row or of the objective function.
	DelRows	Delete rows from a matrix.
	DelSets	Delete sets from a problem.
	Destroy	Obsolete. Destroy the problem, freeing all native resources used. Equivalent to calling Dipose(). (Overrides XPRSobject.Destroy().)
	Dispose	(Inherited from XPRSobject.)
	Drop	Function that is called when the whole problem representation is dropped and will be replaced by a new one soon. This happens for example if readProb is called or any of the load functions are called.
	DumpControls	Displays the list of controls and their current value for those controls that have been set to a non default value.
	Equals	(Inherited from XPRSobject.)
	EstimateRowDualRanges	Performs a dual side range sensitivity analysis, i.e. calculates estimates for the possible ranges for dual values.
	Finalize	(Inherited from XPRSobject.)
	FirstIIS(Int32)	Convenience wrapper for FirstIIS(int,int) that returns the output argument.
	FirstIIS(Int32, Int32)	Initiates a search for an Irreducible Infeasible Set (IIS) in an infeasible problem.
	FixGlobals	Obsolete.
	FixMIPEntities	Fixes all the MIP entities to the values of the last found MIP solution. This is useful for finding the reduced costs for the continuous variables after the integer variables have been fixed to their optimal values.
	FTran	Pre-multiplies a (column) vector provided by the user by the inverse of the current matrix.
	GetAttribInfo	Accesses the id number and the type information of an attribute given its name. An attribute name may be for example XPRS_ROWS. Names are case-insensitive and may or may not have the XPRS_ prefix. The id number is the constant used to identify the attribute for calls to functions such as getIntAttrib. The type information returned will be one of the below integer constants defined in the xprs.h header file. The function will return an id number of 0 and a type value of XPRS_TYPE_NOTDEFINED if the name is not recognized as an attribute name. Note that this will occur if the name is a control name and not an attribute name.
	GetBasis	Returns the current basis into the user's data arrays.
	GetBasisVal	Returns the current basis status for a specific column or row.
	GetCallbackDual(Int32)	Convenience wrapper for bool(out GetCallbackDuals, double[], int, int) that queries only a single value.
	GetCallbackDual(Boolean, Int32)	Convenience wrapper for bool(out GetCallbackDuals, double[], int, int) that queries only a single value.
	GetCallbackDuals()	Convenience wrapper for bool(out GetCallbackDuals, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackDuals(Boolean)	Convenience wrapper for GetCallbackDuals(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackDuals(Int32, Int32)	Convenience wrapper for bool(out GetCallbackDuals, double[], int, int) that allocates the output array.
	GetCallbackDuals(Boolean, Int32, Int32)	Convenience wrapper for bool(out GetCallbackDuals, double[], int, int) that allocates the output array.
	GetCallbackDuals(Boolean, Double, Int32, Int32)	Returns the dual values from the solution associated with the current callback.
	GetCallbackPresolveDual(Int32)	Convenience wrapper for bool(out GetCallbackPresolveDuals, double[], int, int) that queries only a single value.
	GetCallbackPresolveDual(Boolean, Int32)	Convenience wrapper for bool(out GetCallbackPresolveDuals, double[], int, int) that queries only a single value.
	GetCallbackPresolveDuals()	Convenience wrapper for bool(out GetCallbackPresolveDuals, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackPresolveDuals(Boolean)	Convenience wrapper for GetCallbackPresolveDuals(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackPresolveDuals(Int32, Int32)	Convenience wrapper for bool(out GetCallbackPresolveDuals, double[], int, int) that allocates the output array.
	GetCallbackPresolveDuals(Boolean, Int32, Int32)	Convenience wrapper for bool(out GetCallbackPresolveDuals, double[], int, int) that allocates the output array.
	GetCallbackPresolveDuals(Boolean, Double, Int32, Int32)	Returns the dual values from the solution to the presolved problem associated with the current callback.
	GetCallbackPresolveRedCost(Int32)	Convenience wrapper for bool(out GetCallbackPresolveRedCosts, double[], int, int) that queries only a single value.
	GetCallbackPresolveRedCost(Boolean, Int32)	Convenience wrapper for bool(out GetCallbackPresolveRedCosts, double[], int, int) that queries only a single value.
	GetCallbackPresolveRedCosts()	Convenience wrapper for bool(out GetCallbackPresolveRedCosts, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackPresolveRedCosts(Boolean)	Convenience wrapper for GetCallbackPresolveRedCosts(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackPresolveRedCosts(Int32, Int32)	Convenience wrapper for bool(out GetCallbackPresolveRedCosts, double[], int, int) that allocates the output array.
	GetCallbackPresolveRedCosts(Boolean, Int32, Int32)	Convenience wrapper for bool(out GetCallbackPresolveRedCosts, double[], int, int) that allocates the output array.
	GetCallbackPresolveRedCosts(Boolean, Double, Int32, Int32)	Returns the reduced costs from the solution to the presolved problem associated with the current callback.
	GetCallbackPresolveSlack(Int32)	Convenience wrapper for bool(out GetCallbackPresolveSlacks, double[], int, int) that queries only a single value.
	GetCallbackPresolveSlack(Boolean, Int32)	Convenience wrapper for bool(out GetCallbackPresolveSlacks, double[], int, int) that queries only a single value.
	GetCallbackPresolveSlacks()	Convenience wrapper for bool(out GetCallbackPresolveSlacks, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackPresolveSlacks(Boolean)	Convenience wrapper for GetCallbackPresolveSlacks(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackPresolveSlacks(Int32, Int32)	Convenience wrapper for bool(out GetCallbackPresolveSlacks, double[], int, int) that allocates the output array.
	GetCallbackPresolveSlacks(Boolean, Int32, Int32)	Convenience wrapper for bool(out GetCallbackPresolveSlacks, double[], int, int) that allocates the output array.
	GetCallbackPresolveSlacks(Boolean, Double, Int32, Int32)	Returns the slack values from the solution to the presolved problem associated with the current callback.
	GetCallbackPresolveSolution()	Convenience wrapper for bool(out GetCallbackPresolveSolution, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackPresolveSolution(Boolean)	Convenience wrapper for GetCallbackPresolveSolution(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackPresolveSolution(Int32)	Convenience wrapper for bool(out GetCallbackPresolveSolution, double[], int, int) that queries only a single value.
	GetCallbackPresolveSolution(Boolean, Int32)	Convenience wrapper for bool(out GetCallbackPresolveSolution, double[], int, int) that queries only a single value.
	GetCallbackPresolveSolution(Int32, Int32)	Convenience wrapper for bool(out GetCallbackPresolveSolution, double[], int, int) that allocates the output array.
	GetCallbackPresolveSolution(Boolean, Int32, Int32)	Convenience wrapper for bool(out GetCallbackPresolveSolution, double[], int, int) that allocates the output array.
	GetCallbackPresolveSolution(Boolean, Double, Int32, Int32)	Returns the solution to the presolved problem associated with the current callback.
	GetCallbackRedCost(Int32)	Convenience wrapper for bool(out GetCallbackRedCosts, double[], int, int) that queries only a single value.
	GetCallbackRedCost(Boolean, Int32)	Convenience wrapper for bool(out GetCallbackRedCosts, double[], int, int) that queries only a single value.
	GetCallbackRedCosts()	Convenience wrapper for bool(out GetCallbackRedCosts, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackRedCosts(Boolean)	Convenience wrapper for GetCallbackRedCosts(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackRedCosts(Int32, Int32)	Convenience wrapper for bool(out GetCallbackRedCosts, double[], int, int) that allocates the output array.
	GetCallbackRedCosts(Boolean, Int32, Int32)	Convenience wrapper for bool(out GetCallbackRedCosts, double[], int, int) that allocates the output array.
	GetCallbackRedCosts(Boolean, Double, Int32, Int32)	Returns the reduced costs from the solution associated with the current callback.
	GetCallbackSlack(Int32)	Convenience wrapper for bool(out GetCallbackSlacks, double[], int, int) that queries only a single value.
	GetCallbackSlack(Boolean, Int32)	Convenience wrapper for bool(out GetCallbackSlacks, double[], int, int) that queries only a single value.
	GetCallbackSlacks()	Convenience wrapper for bool(out GetCallbackSlacks, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackSlacks(Boolean)	Convenience wrapper for GetCallbackSlacks(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackSlacks(Int32, Int32)	Convenience wrapper for bool(out GetCallbackSlacks, double[], int, int) that allocates the output array.
	GetCallbackSlacks(Boolean, Int32, Int32)	Convenience wrapper for bool(out GetCallbackSlacks, double[], int, int) that allocates the output array.
	GetCallbackSlacks(Boolean, Double, Int32, Int32)	Returns the slack values from the solution associated with the current callback.
	GetCallbackSolution()	Convenience wrapper for bool(out GetCallbackSolution, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackSolution(Boolean)	Convenience wrapper for GetCallbackSolution(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetCallbackSolution(Int32)	Convenience wrapper for bool(out GetCallbackSolution, double[], int, int) that queries only a single value.
	GetCallbackSolution(Boolean, Int32)	Convenience wrapper for bool(out GetCallbackSolution, double[], int, int) that queries only a single value.
	GetCallbackSolution(Int32, Int32)	Convenience wrapper for bool(out GetCallbackSolution, double[], int, int) that allocates the output array.
	GetCallbackSolution(Boolean, Int32, Int32)	Convenience wrapper for bool(out GetCallbackSolution, double[], int, int) that allocates the output array.
	GetCallbackSolution(Boolean, Double, Int32, Int32)	Returns the solution associated with the current callback.
	GetCannotAddConstraints	Error message in case we attempt to create rows with Variables where this is not possible.
	GetCannotAddPWLs	Error message in case we attempt to create rows with Variables where this is not possible.
	GetCannotAddSets	Error message in case we attempt to create sets where this is not possible.
	GetCannotAddVariables	Error message in case we attempt to create variables where this is not possible.
	GetCoef(Int32, Int32)	Convenience wrapper for GetCoef(int,int,double) that returns the output argument.
	GetCoef(Int32, Int32, Double)	Returns a single coefficient in the constraint matrix.
	GetCols(Int32, Int32)	Get range of columns.
	GetCols(Int32, Int32, Double, Int32, Int32, Int32)	Returns the nonzeros in the constraint matrix for the columns in a given range.
	GetCols(Int64, Int32, Double, Int64, Int32, Int32)	Returns the nonzeros in the constraint matrix for the columns in a given range.
	GetCols(Int32, Int32, Double, Int32, Int32, Int32, Int32)	Returns the nonzeros in the constraint matrix for the columns in a given range.
	GetCols(Int64, Int32, Double, Int64, Int64, Int32, Int32)	Returns the nonzeros in the constraint matrix for the columns in a given range.
	GetColType(Int32)	Convenience wrapper for GetColType(char[], int, int).
	GetColType(Int32, Int32)	Convenience wrapper for GetColType(char[], int, int).
	GetColType(Char, Int32, Int32)	Returns the column types for the columns in a given range.
	GetColumnName	Get a column name.
	GetColumnNames	Get names of columns.
	GetControlInfo	Accesses the id number and the type information of a control given its name. A control name may be for example XPRS_PRESOLVE. Names are case-insensitive and may or may not have the XPRS_ prefix. The id number is the constant used to identify the control for calls to functions such as getIntControl. The function will return an id number of 0 and a type value of XPRS_TYPE_NOTDEFINED if the name is not recognized as a control name. Note that this will occur if the name is an attribute name and not a control name.
	GetCPCutList(Int32, Cut, Double)	Returns a list of cut indices from the cut pool.
	GetCPCutList(Int32, Int32, Double, Int32, Cut, Double)	Returns a list of cut indices from the cut pool.
	GetCPCutList(Int32, Int32, Double, Int32, Int32, Cut, Double)	Returns a list of cut indices from the cut pool.
	GetCPCuts(Cut, Int32, Int32, Int32, Char, Int32, Int32, Double, Double)	Returns cuts from the cut pool. A list of cut pointers in the array rowind must be passed to the routine. The columns and elements of the cut will be returned in the regions pointed to by the colind and cutcoef parameters. The columns and elements will be stored contiguously and the starting point of each cut will be returned in the region pointed to by the start parameter.
	GetCPCuts(Cut, Int32, Int64, Int32, Char, Int64, Int32, Double, Double)	Returns cuts from the cut pool. A list of cut pointers in the array rowind must be passed to the routine. The columns and elements of the cut will be returned in the regions pointed to by the colind and cutcoef parameters. The columns and elements will be stored contiguously and the starting point of each cut will be returned in the region pointed to by the start parameter.
	GetCutList(Int32, Cut)	Retrieves a list of cut pointers for the cuts active at the current node.
	GetCutList(Int32, Int32, Int32, Cut)	Retrieves a list of cut pointers for the cuts active at the current node.
	GetCutList(Int32, Int32, Int32, Int32, Cut)	Retrieves a list of cut pointers for the cuts active at the current node.
	GetCutMap	Used to return in which rows a list of cuts are currently loaded into the Optimizer. This is useful for example to retrieve the duals associated with active cuts.
	GetCutSlack(Cut)	Convenience wrapper for GetCutSlack(Cut,double) that returns the output argument.
	GetCutSlack(Cut, Double)	Used to calculate the slack value of a cut with respect to the current LP relaxation solution. The slack is calculated from the cut itself, and might be requested for any cut (even if it is not currently loaded into the problem).
	GetDblAttrib(Int32)	Convenience wrapper for GetDblAttrib(int,double) that returns the output argument.
	GetDblAttrib(Int32, Double)	Enables users to retrieve the values of various double problem attributes. Problem attributes are set during loading and optimization of a problem.
	GetDblControl(Int32)	Convenience wrapper for GetDblControl(int,double) that returns the output argument.
	GetDblControl(Int32, Double)	Retrieves the value of a given double control parameter.
	GetDirs()	Used to return the directives that have been loaded into a matrix. Priorities, forced branching directions and pseudo costs can be returned. If called after presolve, getDirs will get the directives for the presolved problem.
	GetDirs(Int32, Int32, Char, Double, Double)	Used to return the directives that have been loaded into a matrix. Priorities, forced branching directions and pseudo costs can be returned. If called after presolve, getDirs will get the directives for the presolved problem.
	GetDirs(Int32, Int32, Int32, Char, Double, Double)	Used to return the directives that have been loaded into a matrix. Priorities, forced branching directions and pseudo costs can be returned. If called after presolve, getDirs will get the directives for the presolved problem.
	GetDiscreteCols	Get information about MIP entities.
	GetDual(Int32)	Convenience wrapper for int(out GetDuals, double[], int, int) that queries only a single value.
	GetDual(Int32, Int32)	Convenience wrapper for int(out GetDuals, double[], int, int) that queries only a single value.
	GetDualRay	Retrieves a dual ray (dual unbounded direction) for the current problem, if the problem is found to be infeasible.
	GetDuals()	Convenience wrapper for int(out GetDuals, double[], int, int) that allocates the output array and queries all elements.
	GetDuals(Int32)	Convenience wrapper for GetDuals(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetDuals(Int32, Int32)	Convenience wrapper for int(out GetDuals, double[], int, int) that allocates the output array.
	GetDuals(Int32, Int32, Int32)	Convenience wrapper for int(out GetDuals, double[], int, int) that allocates the output array.
	GetDuals(Int32, Double, Int32, Int32)	Used to obtain the dual values associated with the incumbent solution during or after optimization of a continuous problem with optimize, lpOptimize or nlpOptimize.
	GetGenCons(Int32, Int32)	Query a range of general constraints.
	GetGenCons(GenConsType, Int32, Int32, Int32, Int32, Int32, Int32, Double, Int32, Int32, Int32, Int32)	Returns the general constraints y = f(x1, ..., xn, c1, ..., cm) in a given range.
	GetGenCons(GenConsType, Int32, Int64, Int32, Int64, Int64, Int64, Double, Int64, Int64, Int32, Int32)	Returns the general constraints y = f(x1, ..., xn, c1, ..., cm) in a given range.
	GetGenConsName	Get a general constraint name.
	GetGenConsNames	Get names of general constraints.
	GetGlobal	Obsolete.
	GetHashCode	(Inherited from XPRSobject.)
	GetIISData(Int32)	Get information about an IIS.
	GetIISData(Int32, Int32, Int32, Int32, Int32, Char, Char, Double, Double, Char, Char)	Returns information for an Irreducible Infeasible Set: size, variables and constraints (row and column vectors), and conflicting sides of the variables. For pure linear problems there is also information on duals, reduced costs and isolations.
	GetIndex(Int32, String)	Convenience wrapper for GetIndex(int,string,int) that returns the output argument.
	GetIndex(Int32, String, Int32)	Returns the index for a specified row or column name.
	GetIndicator	Get indicator information for a single row.
	GetIndicators(Int32, Int32)	Get indicator information for a range of rows. Returns indicator information for the rows in [ first, last] that actually are indicator rows. No data is returned for rows in this range that are not indicator rows.
	GetIndicators(Int32, Int32, Int32, Int32)	Returns the indicator constraint condition (indicator variable and complement flag) associated to the rows in a given range.
	GetInfeas	Returns a list of infeasible primal and dual variables.
	GetIntAttrib(Int32)	Convenience wrapper for GetIntAttrib(int,int) that returns the output argument.
	GetIntAttrib(Int32, Int32)	Enables users to recover the values of various integer problem attributes. Problem attributes are set during loading and optimization of a problem.
	GetIntControl(Int32)	Convenience wrapper for GetIntControl(int,int) that returns the output argument.
	GetIntControl(Int32, Int32)	Enables users to recover the values of various integer control parameters
	GetLastBarSol()	Get a solution.
	GetLastBarSol(Double, Double, Double, Double, Int32)	Used to obtain the last barrier solution values following optimization that used the barrier solver.
	GetLastBarSolDjs	Get the djs values of a solution.
	GetLastBarSolDuals	Get the duals values of a solution.
	GetLastBarSolSlack	Get the slack values of a solution.
	GetLastBarSolX	Get the x values of a solution.
	GetLastError()	Returns the error message corresponding to the last error encountered by a library function. (Overrides XPRSobject.GetLastError().)
	GetLastError(String)	Returns the error message corresponding to the last error encountered by a library function.
	GetLB(Int32)	Convenience wrapper for GetLB(double[], int, int).
	GetLB(Int32, Int32)	Convenience wrapper for GetLB(double[], int, int).
	GetLB(Double, Int32, Int32)	Returns the lower bounds for the columns in a given range.
	GetLongAttrib(Int32)	Convenience wrapper for GetLongAttrib(int,long) that returns the output argument.
	GetLongAttrib(Int32, Int64)	Enables users to recover the values of various integer problem attributes. Problem attributes are set during loading and optimization of a problem.
	GetLongControl(Int32)	Convenience wrapper for GetLongControl(int,long) that returns the output argument.
	GetLongControl(Int32, Int64)	Enables users to recover the values of various integer control parameters
	GetLpSol()	Get a solution.
	GetLpSol(Double)	Used to obtain the LP solution values following optimization.
	GetLpSol(Double, Double, Double, Double)	Used to obtain the LP solution values following optimization.
	GetLpSolDjs	Get the djs values of a solution.
	GetLpSolDuals	Get the duals values of a solution.
	GetLpSolSlack	Get the slack values of a solution.
	GetLpSolVal	Obsolete. Used to obtain a single LP solution value following optimization.
	GetLpSolX	Get the x values of a solution.
	GetMessageStatus(Int32)	Convenience wrapper for GetMessageStatus(int,int) that returns the output argument.
	GetMessageStatus(Int32, Int32)	Retrieves the current suppression status of a message.
	GetMIPEntities()	Get information about MIP entities and SOS.
	GetMIPEntities(Char, Int32, Double)	Retrieves integr and entity information about a problem. It must be called before mipOptimize if the presolve option is used.
	GetMIPEntities(Int32, Char, Int32, Double)	Retrieves integr and entity information about a problem. It must be called before mipOptimize if the presolve option is used.
	GetMIPEntities(Int32, Int32, Char, Int32, Double, Char, Int32, Int32, Double)	Retrieves integr and entity information about a problem. It must be called before mipOptimize if the presolve option is used.
	GetMIPEntities(Int32, Int32, Char, Int32, Double, Char, Int64, Int32, Double)	Retrieves integr and entity information about a problem. It must be called before mipOptimize if the presolve option is used.
	GetMipSol()	Obsolete. Get a solution.
	GetMipSol(Double)	Obsolete. Used to obtain the solution values of the last MIP solution that was found.
	GetMipSol(Double, Double)	Obsolete. Used to obtain the solution values of the last MIP solution that was found.
	GetMipSolSlack	Obsolete. Get the slack values of a solution.
	GetMipSolVal	Obsolete. Used to obtain a single solution value of the last MIP solution that was found.
	GetMipSolX	Obsolete. Get the x values of a solution.
	GetMQObj(Int32, Int32)	Get quadratic objective matrix for range of columns.
	GetMQObj(Int32, Int32, Double, Int32, Int32, Int32)	Returns the nonzeros in the quadratic objective coefficients matrix for the columns in a given range. To achieve maximum efficiency, getMQObj returns the lower triangular part of this matrix only.
	GetMQObj(Int64, Int32, Double, Int64, Int32, Int32)	Returns the nonzeros in the quadratic objective coefficients matrix for the columns in a given range. To achieve maximum efficiency, getMQObj returns the lower triangular part of this matrix only.
	GetMQObj(Int32, Int32, Double, Int32, Int32, Int32, Int32)	Returns the nonzeros in the quadratic objective coefficients matrix for the columns in a given range. To achieve maximum efficiency, getMQObj returns the lower triangular part of this matrix only.
	GetMQObj(Int64, Int32, Double, Int64, Int64, Int32, Int32)	Returns the nonzeros in the quadratic objective coefficients matrix for the columns in a given range. To achieve maximum efficiency, getMQObj returns the lower triangular part of this matrix only.
	GetName(Int32, Int32)	Get the name of a single element.
	GetName(Namespaces, Int32)	Get the name of a single element.
	GetNameList(Int32, Int32, Int32)	Get the given row, column or set names for the given range.
	GetNameList(Int32, String, Int32, Int32)	Get the given row, column or set names for the given range.
	GetNameListObject
	GetNames(Int32, Int32, Int32)	Get names. Retrieves names for a certain type of objects.
	GetNames(Namespaces, Int32, Int32)	Get names. Retrieves names for a certain type of objects.
	GetNames(Int32, String, Int32, Int32)	Get the given row, column or set names for the given range.
	GetNames(Namespaces, String, Int32, Int32)	Get names. Retrieves names for a certain type of objects.
	GetNlpsol	Obsolete. Obtain the current SLP solution values
	GetObj(Int32)	Convenience wrapper for GetObj(double[], int, int).
	GetObj(Int32, Int32)	Convenience wrapper for GetObj(double[], int, int).
	GetObj(Double, Int32, Int32)	Returns the objective function coefficients for the columns in a given range.
	GetObjDblAttrib(Int32, Int32)	Convenience wrapper for GetObjDblAttrib(int,int,double) that returns the output argument.
	GetObjDblAttrib(Int32, Int32, Double)	Retrieves the value of a given double attribute associated with a multi-objective solve. When solving a multi-objective problem, several objectives might be optimized in sequence. After each solve, the problem attributes are captured so that they can be queried afterwards.
	GetObjDblControl(Int32, ObjControl)	Convenience wrapper for GetObjDblControl(int,ObjControl,double) that returns the output argument.
	GetObjDblControl(Int32, ObjControl, Double)	Retrieves the value of a given double control parameter associated with an objective function. These parameters control how the objective is treated during multi-objective optimization.
	GetObjectTypeName	Function to access the type name of an object referenced using the generic Optimizer object pointer XPRSobject. (Inherited from XPRSobject.)
	GetObjIntAttrib(Int32, Int32)	Convenience wrapper for GetObjIntAttrib(int,int,int) that returns the output argument.
	GetObjIntAttrib(Int32, Int32, Int32)	Retrieves the value of a given integer attribute associated with a multi-objective solve. When solving a multi-objective problem, several objectives might be optimized in sequence. After each solve, the problem attributes are captured so that they can be queried afterwards.
	GetObjIntAttrib(Int32, Int32, Int64)	Retrieves the value of a given integer attribute associated with a multi-objective solve. When solving a multi-objective problem, several objectives might be optimized in sequence. After each solve, the problem attributes are captured so that they can be queried afterwards.
	GetObjIntControl(Int32, ObjControl)	Convenience wrapper for GetObjIntControl(int,ObjControl,int) that returns the output argument.
	GetObjIntControl(Int32, ObjControl, Int32)	Retrieves the value of a given integer control parameter associated with an objective. These parameters control how the objective is treated during multi-objective optimization.
	GetObjLongAttrib	Convenience wrapper for GetObjIntAttrib(int,int,long) that returns the output argument.
	GetObjN	For a given objective function, returns the objective coefficients for the columns in a given range.
	GetPivotOrder	Returns the pivot order of the basic variables.
	GetPivots	Returns a list of potential leaving variables if a specified variable enters the basis.
	GetPresolveBasis	Returns the current basis from memory into the user's data areas. If the problem is presolved, the presolved basis will be returned. Otherwise the original basis will be returned.
	GetPresolveMap	Returns the mapping of the row and column numbers from the presolve problem back to the original problem.
	GetPresolveSol()	Get a solution.
	GetPresolveSol(Double)	Returns the solution for the presolved problem from memory.
	GetPresolveSol(Double, Double, Double, Double)	Returns the solution for the presolved problem from memory.
	GetPresolveSolDjs	Get the djs values of a solution.
	GetPresolveSolDuals	Get the duals values of a solution.
	GetPresolveSolSlack	Get the slack values of a solution.
	GetPresolveSolX	Get the x values of a solution.
	GetPrimalRay	Retrieves a primal ray (primal unbounded direction) for the current problem, if the problem is found to be unbounded.
	GetProbName()	Returns the current problem name.
	GetProbName(String)	Returns the current problem name.
	GetPwlCons(Int32, Int32, Int32, Double, Double, Int32, Int32, Int32, Int32)	Returns the piecewise linear constraints y = f(x) in a given range.
	GetPwlCons(Int32, Int32, Int64, Double, Double, Int64, Int64, Int32, Int32)	Returns the piecewise linear constraints y = f(x) in a given range.
	GetPWLName	Get a PWL constraint name.
	GetPWLNames	Get names of PWL constraints.
	GetQObj(Int32, Int32)	Convenience wrapper for GetQObj(int,int,double) that returns the output argument.
	GetQObj(Int32, Int32, Double)	Returns a single quadratic objective function coefficient corresponding to the variable pair (objqcol1, objqcol2) of the Hessian matrix.
	GetQRowCoeff(Int32, Int32, Int32)	Convenience wrapper for GetQRowCoeff(int,int,int,double) that returns the output argument.
	GetQRowCoeff(Int32, Int32, Int32, Double)	Returns a single quadratic constraint coefficient corresponding to the variable pair ( rowqcol1, rowqcol2) of the Hessian of a given constraint.
	GetQRowQMatrix(Int32, Int32, Int32, Double, Int32, Int32, Int32)	Returns the nonzeros in a quadratic constraint coefficients matrix for the columns in a given range. To achieve maximum efficiency, getQRowQMatrix returns the lower triangular part of this matrix only.
	GetQRowQMatrix(Int32, Int32, Int32, Double, Int32, Int32, Int32, Int32)	Returns the nonzeros in a quadratic constraint coefficients matrix for the columns in a given range. To achieve maximum efficiency, getQRowQMatrix returns the lower triangular part of this matrix only.
	GetQRowQMatrixTriplets(Int32, Int32, Int32, Double)	Returns the nonzeros in a quadratic constraint coefficients matrix as triplets (index pairs with coefficients). To achieve maximum efficiency, getQRowQMatrixTriplets returns the lower triangular part of this matrix only.
	GetQRowQMatrixTriplets(Int32, Int32, Int32, Int32, Double)	Returns the nonzeros in a quadratic constraint coefficients matrix as triplets (index pairs with coefficients). To achieve maximum efficiency, getQRowQMatrixTriplets returns the lower triangular part of this matrix only.
	GetQRows()	Returns the list indices of the rows that have quadratic coefficients.
	GetQRows(Int32)	Returns the list indices of the rows that have quadratic coefficients.
	GetQRows(Int32, Int32)	Returns the list indices of the rows that have quadratic coefficients.
	GetRedCost(Int32)	Convenience wrapper for int(out GetRedCosts, double[], int, int) that queries only a single value.
	GetRedCost(Int32, Int32)	Convenience wrapper for int(out GetRedCosts, double[], int, int) that queries only a single value.
	GetRedCosts()	Convenience wrapper for int(out GetRedCosts, double[], int, int) that allocates the output array and queries all elements.
	GetRedCosts(Int32)	Convenience wrapper for GetRedCosts(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetRedCosts(Int32, Int32)	Convenience wrapper for int(out GetRedCosts, double[], int, int) that allocates the output array.
	GetRedCosts(Int32, Int32, Int32)	Convenience wrapper for int(out GetRedCosts, double[], int, int) that allocates the output array.
	GetRedCosts(Int32, Double, Int32, Int32)	Used to obtain the reduced costs associated with the incumbent solution during or after optimization of a continuous problem with optimize, lpOptimize or nlpOptimize.
	GetRHS(Int32)	Convenience wrapper for GetRHS(double[], int, int).
	GetRHS(Int32, Int32)	Convenience wrapper for GetRHS(double[], int, int).
	GetRHS(Double, Int32, Int32)	Returns the right hand side elements for the rows in a given range.
	GetRHSRange(Int32)	Convenience wrapper for GetRHSRange(double[], int, int).
	GetRHSRange(Int32, Int32)	Convenience wrapper for GetRHSRange(double[], int, int).
	GetRHSRange(Double, Int32, Int32)	Returns the right hand side range values for the rows in a given range.
	GetRowFlags(Int32)	Convenience wrapper for GetRowFlags(int[], int, int).
	GetRowFlags(Int32, Int32)	Convenience wrapper for GetRowFlags(int[], int, int).
	GetRowFlags(Int32, Int32, Int32)	Retrieve if a range of rows have been set up as special rows.
	GetRowName	Get a row name.
	GetRowNames	Get names of rows.
	GetRows(Int32, Int32)	Get range of rows.
	GetRows(Int32, Int32, Double, Int32, Int32, Int32)	Returns the nonzeros in the constraint matrix for the rows in a given range.
	GetRows(Int64, Int32, Double, Int64, Int32, Int32)	Returns the nonzeros in the constraint matrix for the rows in a given range.
	GetRows(Int32, Int32, Double, Int32, Int32, Int32, Int32)	Returns the nonzeros in the constraint matrix for the rows in a given range.
	GetRows(Int64, Int32, Double, Int64, Int64, Int32, Int32)	Returns the nonzeros in the constraint matrix for the rows in a given range.
	GetRowType(Int32)	Convenience wrapper for GetRowType(char[], int, int).
	GetRowType(Int32, Int32)	Convenience wrapper for GetRowType(char[], int, int).
	GetRowType(Char, Int32, Int32)	Returns the row types for the rows in a given range.
	GetScale	Returns the the current scaling of the matrix.
	GetScaledInfeas	Returns a list of scaled infeasible primal and dual variables for the original problem. If the problem is currently presolved, it is postsolved before the function returns.
	GetSetDefinitions	Get information about SOS.
	GetSetName	Get a Set (SOS) name.
	GetSetNames	Get names of Sets (SOS).
	GetSets	Get information about SOS.
	GetSlack(Int32)	Convenience wrapper for int(out GetSlacks, double[], int, int) that queries only a single value.
	GetSlack(Int32, Int32)	Convenience wrapper for int(out GetSlacks, double[], int, int) that queries only a single value.
	GetSlacks()	Convenience wrapper for int(out GetSlacks, double[], int, int) that allocates the output array and queries all elements.
	GetSlacks(Int32)	Convenience wrapper for GetSlacks(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetSlacks(Int32, Int32)	Convenience wrapper for int(out GetSlacks, double[], int, int) that allocates the output array.
	GetSlacks(Int32, Int32, Int32)	Convenience wrapper for int(out GetSlacks, double[], int, int) that allocates the output array.
	GetSlacks(Int32, Double, Int32, Int32)	Used to obtain the slack values associated with the incumbent solution during or after optimization with optimize, mipOptimize, lpOptimize or nlpOptimize.
	GetSolDjs	Obsolete. Get the djs values of a solution.
	GetSolDuals	Obsolete. Get the duals values of a solution.
	GetSolSlack	Obsolete. Get the slack values of a solution.
	GetSolution()	Convenience wrapper for int(out GetSolution, double[], int, int) that allocates the output array and queries all elements.
	GetSolution(Int32)	Convenience wrapper for int(out GetSolution, double[], int, int) that queries only a single value.
	GetSolution(Int32)	Convenience wrapper for GetSolution(IntHolder, double[], int, int) that allocates the output array and queries all elements.
	GetSolution(Int32, Int32)	Convenience wrapper for int(out GetSolution, double[], int, int) that allocates the output array.
	GetSolution(Int32, Int32)	Convenience wrapper for int(out GetSolution, double[], int, int) that queries only a single value.
	GetSolution(Int32, Int32, Int32)	Convenience wrapper for int(out GetSolution, double[], int, int) that allocates the output array.
	GetSolution(Int32, Double, Int32, Int32)	Used to obtain the incumbent solution during or after optimization with optimize, mipOptimize, lpOptimize or nlpOptimize.
	GetSolX	Obsolete. Get the x values of a solution.
	GetStrAttrib(Int32)	Get the current value of a string attribute
	GetStrAttrib(Int32, String)	Get the current value of a string attribute
	GetStrControl(Int32)	Get the current value of a string control
	GetStrControl(Int32, String)	Get the current value of a string control
	GetStringControl	Returns the value of a given string control parameters.
	GetStrStringAttrib	Enables users to recover the values of various string problem attributes. Problem attributes are set during loading and optimization of a problem.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	GetUB(Int32)	Convenience wrapper for GetUB(double[], int, int).
	GetUB(Int32, Int32)	Convenience wrapper for GetUB(double[], int, int).
	GetUB(Double, Int32, Int32)	Returns the upper bounds for the columns in a given range.
	GetUnbVec()	Convenience wrapper for GetUnbVec(int) that returns the output argument.
	GetUnbVec(Int32)	Returns the index vector which causes the primal simplex or dual simplex algorithm to determine that a matrix is primal or dual unbounded respectively.
	IISAll	Performs an automated search for independent Irreducible Infeasible Sets (IIS) in an infeasible problem.
	IISIsolations	Performs the isolation identification procedure for an Irreducible Infeasible Set (IIS). This function applies only to linear problems.
	IISStatus()	Get the IIS status.
	IISStatus(Int32, Int32, Int32, Double, Int32)	Returns statistics on the Irreducible Infeasible Sets (IIS) found so far by firstIIS ( IIS), nextIIS ( IIS -n) or iISAll ( IIS -a).
	Interrupt()	Interrupts the Optimizer algorithms.
	Interrupt(StopType)	Interrupts the Optimizer algorithms.
	IntVar()	Create a new integer variable with default bounds [0, infinity].
	IntVar(String)	Create a new integer variable with default bounds [0, infinity] and a specified name.
	IntVar(Double, Double)	Create a new integer variable with specified bounds.
	IntVar(Double, Double, String)	Create a new integer variable with specified bounds and name.
	IntVarArray(Int32, Double, Double, Func<Int32, String>)	Create an array of integer variables. All the variables created by this function have the same types and bounds.
	IntVarArray(Int32, Double, Double, String)	Create an array of integer variables.
	IntVarArray(Int32, Func<Int32, Double>, Func<Int32, Double>, Func<Int32, String>)	Create an array of integer variables.
	IntVarArray<T>(ICollection<T>, Double, Double, Func<T, String>)	Create an array of integer variables. All the variables created by this function have the same types and bounds. The function will create one variable for each of the objects listed in objs.
	IntVarArray<T>(ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>)	Create an array of integer variables. The function will create one variable for each of the objects listed in objs.
	IntVarMap<T>(ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>)	Create a map of integer variables. The function creates a new variable for each object in objs. It returns a hash map in which each object in objs maps to the index of the variable that was created for it.
	IntVarMap<T>(ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>, IDictionary<T, Int32>)	Create a map of integer variables. The function creates a new variable for each object in objs. For each object o it puts the Pair (o, idx) into map where idx is the index of the variable that was created for o.
	LoadBasis	Loads a basis from the user's areas.
	LoadBranchDirs(Int32, Int32)	Convenience wrapper for LoadBranchDirs(int, int[], int[]).
	LoadBranchDirs(Int32, Int32, Int32)	Loads directives into the current problem to specify which MIP entities the Optimizer should continue to branch on when a node solution is integer feasible.
	LoadCuts(Int32, Cut)	Loads cuts from the cut pool into the matrix. Without calling loadCuts the cuts will remain in the cut pool but will not be active at the node. Cuts loaded at a node remain active at all descendant nodes unless they are deleted using delCuts.
	LoadCuts(Int32, Int32)	Loads cuts from the cut pool into the matrix. Without calling loadCuts the cuts will remain in the cut pool but will not be active at the node. Cuts loaded at a node remain active at all descendant nodes unless they are deleted using delCuts.
	LoadCuts(Int32, Int32, Int32, Cut)	Loads cuts from the cut pool into the matrix. Without calling loadCuts the cuts will remain in the cut pool but will not be active at the node. Cuts loaded at a node remain active at all descendant nodes unless they are deleted using delCuts.
	LoadDelayedRows	Specifies that a set of rows in the matrix will be treated as delayed rows during a tree search. These are rows that must be satisfied for any integer solution, but will not be loaded into the active set of constraints until required.
	LoadDirs	Loads directives into the matrix.
	LoadGlobal(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double, Int32, Int32, Char, Int32, Double, Char, Int32, Int32, Double)	Obsolete.
	LoadGlobal(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double, Int32, Int32, Char, Int32, Double, Char, Int64, Int32, Double)	Obsolete.
	LoadLP(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double)	Enables the user to pass a matrix directly to the Optimizer, rather than reading the matrix from a file.
	LoadLP(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double)	Enables the user to pass a matrix directly to the Optimizer, rather than reading the matrix from a file.
	LoadLPSol(Double, Double, Double, Double)	Convenience wrapper for LoadLPSol(double[],double[],double[],double[],int) that returns the output argument.
	LoadLPSol(Double, Double, Double, Double, Int32)	Loads an LP solution for the problem into the Optimizer.
	LoadMIP(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double, Int32, Int32, Char, Int32, Double, Char, Int32, Int32, Double)	Used to load a MIP problem into the Optimizer data structures. Integer, binary, partial integer, semi-continuous and semi-continuous integer variables can be defined, together with sets of type 1 and 2. The reference row values for the set members are passed as an array rather than specifying a reference row.
	LoadMIP(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double, Int32, Int32, Char, Int32, Double, Char, Int64, Int32, Double)	Used to load a MIP problem into the Optimizer data structures. Integer, binary, partial integer, semi-continuous and semi-continuous integer variables can be defined, together with sets of type 1 and 2. The reference row values for the set members are passed as an array rather than specifying a reference row.
	LoadMipSol(Double)	Convenience wrapper for LoadMipSol(double[],int) that returns the output argument.
	LoadMipSol(Double, Int32)	Loads a starting MIP solution for the problem into the Optimizer.
	LoadMIQCQP(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double, Int32, Int32, Int32, Double, Int32, Int32, Int32, Int32, Int32, Double, Int32, Int32, Char, Int32, Double, Char, Int32, Int32, Double)	Used to load a mixed integer quadratic problem with quadratic constraints into the Optimizer data structure. Such a problem may have quadratic terms in its objective function as well as in its constraints. Integer, binary, partial integer, semi-continuous and semi-continuous integer variables can be defined, together with sets of type 1 and 2. The reference row values for the set members are passed as an array rather than specifying a reference row.
	LoadMIQCQP(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double, Int64, Int32, Int32, Double, Int32, Int32, Int64, Int32, Int32, Double, Int32, Int32, Char, Int32, Double, Char, Int64, Int32, Double)	Used to load a mixed integer quadratic problem with quadratic constraints into the Optimizer data structure. Such a problem may have quadratic terms in its objective function as well as in its constraints. Integer, binary, partial integer, semi-continuous and semi-continuous integer variables can be defined, together with sets of type 1 and 2. The reference row values for the set members are passed as an array rather than specifying a reference row.
	LoadMIQP(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double, Int32, Int32, Int32, Double, Int32, Int32, Char, Int32, Double, Char, Int32, Int32, Double)	Used to load a MIQP problem, hence a MIP with quadratic objective coefficients, into the Optimizer data structures. Integer, binary, partial integer, semi-continuous and semi-continuous integer variables can be defined, together with sets of type 1 and 2. The reference row values for the set members are passed as an array rather than specifying a reference row.
	LoadMIQP(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double, Int64, Int32, Int32, Double, Int32, Int32, Char, Int32, Double, Char, Int64, Int32, Double)	Used to load a MIQP problem, hence a MIP with quadratic objective coefficients, into the Optimizer data structures. Integer, binary, partial integer, semi-continuous and semi-continuous integer variables can be defined, together with sets of type 1 and 2. The reference row values for the set members are passed as an array rather than specifying a reference row.
	LoadModelCuts	Specifies that a set of rows in the matrix will be treated as model cuts.
	LoadPresolveBasis	Loads a presolved basis from the user's areas.
	LoadPresolveDirs	Loads directives into the presolved matrix.
	LoadQCQP(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double, Int32, Int32, Int32, Double, Int32, Int32, Int32, Int32, Int32, Double)	Used to load a quadratic problem with quadratic side constraints into the Optimizer data structure. Such a problem may have quadratic terms in its objective function as well as in its constraints.
	LoadQCQP(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double, Int64, Int32, Int32, Double, Int32, Int32, Int64, Int32, Int32, Double)	Used to load a quadratic problem with quadratic side constraints into the Optimizer data structure. Such a problem may have quadratic terms in its objective function as well as in its constraints.
	LoadQCQPGlobal(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double, Int32, Int32, Int32, Double, Int32, Int32, Int32, Int32, Int32, Double, Int32, Int32, Char, Int32, Double, Char, Int32, Int32, Double)	Obsolete.
	LoadQCQPGlobal(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double, Int64, Int32, Int32, Double, Int32, Int32, Int64, Int32, Int32, Double, Int32, Int32, Char, Int32, Double, Char, Int64, Int32, Double)	Obsolete.
	LoadQGlobal(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double, Int32, Int32, Int32, Double, Int32, Int32, Char, Int32, Double, Char, Int32, Int32, Double)	Obsolete.
	LoadQGlobal(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double, Int64, Int32, Int32, Double, Int32, Int32, Char, Int32, Double, Char, Int64, Int32, Double)	Obsolete.
	LoadQP(String, Int32, Int32, Char, Double, Double, Double, Int32, Int32, Int32, Double, Double, Double, Int32, Int32, Int32, Double)	Used to load a quadratic problem into the Optimizer data structure. Such a problem may have quadratic terms in its objective function, although not in its constraints.
	LoadQP(String, Int32, Int32, Char, Double, Double, Double, Int64, Int32, Int32, Double, Double, Double, Int64, Int32, Int32, Double)	Used to load a quadratic problem into the Optimizer data structure. Such a problem may have quadratic terms in its objective function, although not in its constraints.
	LoadSecureVecs	Allows the user to mark rows and columns in order to prevent the presolve removing these rows and columns from the matrix.
	LpOptimize()	This function begins a search for the optimal continuous (LP) solution. The direction of optimization is given by OBJSENSE. The status of the problem when the function completes can be checked using LPSTATUS. Any MIP entities in the problem will be ignored.
	LpOptimize(String)	This function begins a search for the optimal continuous (LP) solution. The direction of optimization is given by OBJSENSE. The status of the problem when the function completes can be checked using LPSTATUS. Any MIP entities in the problem will be ignored.
	Maxim()	Obsolete. Begins a search for the optimal LP solution.
	Maxim(String)	Obsolete. Begins a search for the optimal LP solution.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Minim()	Obsolete. Begins a search for the optimal LP solution.
	Minim(String)	Obsolete. Begins a search for the optimal LP solution.
	MipOptimize()	This function begins a tree search for the optimal MIP solution. The direction of optimization is given by OBJSENSE. The status of the problem when the function completes can be checked using MIPSTATUS.
	MipOptimize(String)	This function begins a tree search for the optimal MIP solution. The direction of optimization is given by OBJSENSE. The status of the problem when the function completes can be checked using MIPSTATUS.
	MsAddCustomPreset	A combined version of XSLPmsaddjob and XSLPmsaddpreset. The preset described is loaded, topped up with the specific settings supplied
	MsAddJob	Adds a multistart job to the multistart pool
	MsAddPreset	Loads a preset of jobs into the multistart job pool.
	MsClear	Removes all scheduled jobs from the multistart job pool
	NextIIS()	Convenience wrapper for NextIIS(int) that returns the output argument.
	NextIIS(Int32)	Continues the search for further Irreducible Infeasible Sets (IIS), or calls firstIIS ( IIS) if no IIS has been identified yet.
	NlpAddFormulas	Add non-linear formulas to the SLP problem.
	NlpCalcSlacks	Calculate the slack values for the provided solution in the non-linear problem
	NlpChgFormula	Add or replace a single matrix formula using a parsed or unparsed formula
	NlpChgFormulaStr	Add or replace a single matrix formula using a character string for the formula.
	NlpChgFormulaString	Obsolete. Add or replace a single matrix formula using a character string for the formula.
	NlpCurrentIV	Transfer the current solution to initial values
	NlpDelFormulas	Delete nonlinear formulas from the current problem
	NlpDelUserFunction	Delete a user function from the current problem
	NlpEvaluateFormula(Int32, Int32, Double)	Convenience wrapper for NlpEvaluateFormula(int,int[],double[],double) that returns the output argument.
	NlpEvaluateFormula(Int32, Int32, Double, Double)	Evaluate a formula using the current values of the variables
	NlpGetFormula	Retrieve a single matrix formula as a formula split into tokens.
	NlpGetFormulaRows	Retrieve the list of positions of the nonlinear formulas in the problem
	NlpGetFormulaStr	Retrieve a single matrix formula in a character string.
	NlpGetFormulaString	Obsolete. Retrieve a single matrix formula in a character string.
	NlpImportLibFunc	Imports a function from a library file to be called as a user function
	NlpLoadFormulas	Load non-linear formulas into the SLP problem
	NlpOptimize	Maximize or minimize an SLP problem
	NlpPostsolveProb	Restores the problem to its pre-solve state
	NlpPrintEvalInfo	Print a summary of any evaluation errors that may have occurred during solving a problem
	NlpSetFunctionError	Set the function error flag for the problem
	NlpSetInitVal(Int32, Double)	Convenience wrapper for NlpSetInitVal(int, int[], double[]).
	NlpSetInitVal(Int32, Int32, Double)	Set the initial value of a variable
	NlpValidate	Validate the feasibility of constraints in a converged solution
	NlpValidateKKT	Validates the first order optimality conditions also known as the Karush-Kuhn-Tucker (KKT) conditions versus the currect solution
	NlpValidateRow	Prints an extensive analysis on a given constraint of the SLP problem
	NlpValidateVector	Validate the feasibility of constraints for a given solution
	Objsa	Returns upper and lower sensitivity ranges for specified objective function coefficients. If the objective coefficients are varied within these ranges the current basis remains optimal and the reduced costs remain valid.
	Optimize()	Convenience wrapper for .Optimize(string , out int, out int). This function calls optimize with default arguments and returns the solve status. The solution status is returned and must be queried explicitly afterwards using the corresponding attribute. See the documentation of the overloaded function for more details.
	Optimize(String)	Convenience wrapper for .Optimize(string , out int, out int). This function calls optimize with the specified flags and returns the solve status. The solution status is returned and must be queried explicitly afterwards using the corresponding attribute. See the documentation of the overloaded function for more details.
	Optimize(String, Int32, Int32)	This function begins a search for the optimal solution of the problem. The direction of optimization is given by OBJSENSE.
	Pivot	Performs a simplex pivot by bringing variable enter into the basis and removing leave.
	PostSolve	Postsolve the current matrix when it is in a presolved state.
	PostSolveSol	Postsolves a primal solution formulated in the presolved space into the corresponding solution formulated in the original space. The problem itself is unchanged.
	PresolveRow(XPRSprob.RowInfo)	Presolve a row. The function transforms a row that is given in terms of the original model to a row that is given in terms of the presolved model.
	PresolveRow(Int32, Double, Char, Double)	Presolve a row. The function transforms a row that is given in terms of the original model to a row that is given in terms of the presolved model.
	PresolveRow(Char, Int32, Int32, Double, Double, Int32, Int32, Int32, Double, Double, Int32)	Presolves a row formulated in terms of the original variables such that it can be added to a presolved matrix.
	PrintIIS	Prints a given Irreducible Infeasible Set (IIS) in the log. If 0 is passed as the IIS number parameter, the initial infeasible subproblem is printed.
	ReadBasis()	Instructs the Optimizer to read in a previously saved basis from a file.
	ReadBasis(String)	Instructs the Optimizer to read in a previously saved basis from a file.
	ReadBasis(String, String)	Instructs the Optimizer to read in a previously saved basis from a file.
	ReadBinSol()	Reads a solution from a binary solution file.
	ReadBinSol(String)	Reads a solution from a binary solution file.
	ReadBinSol(String, String)	Reads a solution from a binary solution file.
	ReadDirs()	Reads a directives file to help direct the tree search.
	ReadDirs(String)	Reads a directives file to help direct the tree search.
	ReadProb(String)	Reads an (X)MPS or LP format matrix from file.
	ReadProb(String, String)	Reads an (X)MPS or LP format matrix from file.
	ReadSlxSol()	Reads an ASCII solution file [ .slx] created by the writeSlxSol function.
	ReadSlxSol(String)	Reads an ASCII solution file [ .slx] created by the writeSlxSol function.
	ReadSlxSol(String, String)	Reads an ASCII solution file [ .slx] created by the writeSlxSol function.
	RefineMipSol	Obsolete. Executes the MIP solution refiner.
	RemoveAfterObjectiveCallback	Remove AfterObjective callback.
	RemoveAfterObjectiveCallbacks	Remove all AfterObjective callbacks.
	RemoveBarIterationCallback	Remove BarIteration callback.
	RemoveBarIterationCallbacks	Remove all BarIteration callbacks.
	RemoveBarlogCallback	Remove Barlog callback.
	RemoveBarlogCallbacks	Remove all Barlog callbacks.
	RemoveBeforeObjectiveCallback	Remove BeforeObjective callback.
	RemoveBeforeObjectiveCallbacks	Remove all BeforeObjective callbacks.
	RemoveBeforeSolveCallback	Remove BeforeSolve callback.
	RemoveBeforeSolveCallbacks	Remove all BeforeSolve callbacks.
	RemoveChangeBranchObjectCallback	Remove ChangeBranchObject callback.
	RemoveChangeBranchObjectCallbacks	Remove all ChangeBranchObject callbacks.
	RemoveCheckTimeCallback	Remove CheckTime callback.
	RemoveCheckTimeCallbacks	Remove all CheckTime callbacks.
	RemoveChgbranchCallback	Obsolete. Remove Chgbranch callback.
	RemoveChgbranchCallbacks	Obsolete. Remove all Chgbranch callbacks.
	RemoveChgnodeCallback	Obsolete. Remove Chgnode callback.
	RemoveChgnodeCallbacks	Obsolete. Remove all Chgnode callbacks.
	RemoveComputeRestartCallback	Remove ComputeRestart callback.
	RemoveComputeRestartCallbacks	Remove all ComputeRestart callbacks.
	RemoveCutlogCallback	Remove Cutlog callback.
	RemoveCutlogCallbacks	Remove all Cutlog callbacks.
	RemoveCutmgrCallback	Obsolete. Remove Cutmgr callback.
	RemoveCutmgrCallbacks	Obsolete. Remove all Cutmgr callbacks.
	RemoveGapNotifyCallback	Remove GapNotify callback.
	RemoveGapNotifyCallbacks	Remove all GapNotify callbacks.
	RemoveInfnodeCallback	Remove Infnode callback.
	RemoveInfnodeCallbacks	Remove all Infnode callbacks.
	RemoveIntsolCallback	Remove Intsol callback.
	RemoveIntsolCallbacks	Remove all Intsol callbacks.
	RemoveLplogCallback	Remove Lplog callback.
	RemoveLplogCallbacks	Remove all Lplog callbacks.
	RemoveMessageCallback	Remove Message callback.
	RemoveMessageCallbacks	Remove all Message callbacks.
	RemoveMiplogCallback	Remove Miplog callback.
	RemoveMiplogCallbacks	Remove all Miplog callbacks.
	RemoveMipThreadCallback	Remove MipThread callback.
	RemoveMipThreadCallbacks	Remove all MipThread callbacks.
	RemoveMipThreadDestroyCallback	Remove MipThreadDestroy callback.
	RemoveMipThreadDestroyCallbacks	Remove all MipThreadDestroy callbacks.
	RemoveMsgHandlerCallback	Remove MsgHandler callback. (Overrides XPRSobject.RemoveMsgHandlerCallback(MsgHandlerCallback).)
	RemoveMsgHandlerCallbacks	Remove all MsgHandler callbacks. (Overrides XPRSobject.RemoveMsgHandlerCallbacks().)
	RemoveMsJobEndCallback	Remove MsJobEnd callback.
	RemoveMsJobEndCallbacks	Remove all MsJobEnd callbacks.
	RemoveMsJobStartCallback	Remove MsJobStart callback.
	RemoveMsJobStartCallbacks	Remove all MsJobStart callbacks.
	RemoveMsWinnerCallback	Remove MsWinner callback.
	RemoveMsWinnerCallbacks	Remove all MsWinner callbacks.
	RemoveNewnodeCallback	Remove Newnode callback.
	RemoveNewnodeCallbacks	Remove all Newnode callbacks.
	RemoveNlpCoefEvalErrorCallback	Remove NlpCoefEvalError callback.
	RemoveNlpCoefEvalErrorCallbacks	Remove all NlpCoefEvalError callbacks.
	RemoveNodecutoffCallback	Remove Nodecutoff callback.
	RemoveNodecutoffCallbacks	Remove all Nodecutoff callbacks.
	RemoveNodeLPSolvedCallback	Remove NodeLPSolved callback.
	RemoveNodeLPSolvedCallbacks	Remove all NodeLPSolved callbacks.
	RemoveOptnodeCallback	Remove Optnode callback.
	RemoveOptnodeCallbacks	Remove all Optnode callbacks.
	RemovePreIntsolCallback	Remove PreIntsol callback.
	RemovePreIntsolCallbacks	Remove all PreIntsol callbacks.
	RemovePrenodeCallback	Remove Prenode callback.
	RemovePrenodeCallbacks	Remove all Prenode callbacks.
	RemovePresolveCallback	Remove Presolve callback.
	RemovePresolveCallbacks	Remove all Presolve callbacks.
	RemoveSlpCascadeEndCallback	Remove SlpCascadeEnd callback.
	RemoveSlpCascadeEndCallbacks	Remove all SlpCascadeEnd callbacks.
	RemoveSlpCascadeStartCallback	Remove SlpCascadeStart callback.
	RemoveSlpCascadeStartCallbacks	Remove all SlpCascadeStart callbacks.
	RemoveSlpCascadeVarCallback	Remove SlpCascadeVar callback.
	RemoveSlpCascadeVarCallbacks	Remove all SlpCascadeVar callbacks.
	RemoveSlpCascadeVarFailCallback	Remove SlpCascadeVarFail callback.
	RemoveSlpCascadeVarFailCallbacks	Remove all SlpCascadeVarFail callbacks.
	RemoveSlpConstructCallback	Remove SlpConstruct callback.
	RemoveSlpConstructCallbacks	Remove all SlpConstruct callbacks.
	RemoveSlpDrColCallback	Remove SlpDrCol callback.
	RemoveSlpDrColCallbacks	Remove all SlpDrCol callbacks.
	RemoveSlpIntSolCallback	Remove SlpIntSol callback.
	RemoveSlpIntSolCallbacks	Remove all SlpIntSol callbacks.
	RemoveSlpIterEndCallback	Remove SlpIterEnd callback.
	RemoveSlpIterEndCallbacks	Remove all SlpIterEnd callbacks.
	RemoveSlpIterStartCallback	Remove SlpIterStart callback.
	RemoveSlpIterStartCallbacks	Remove all SlpIterStart callbacks.
	RemoveSlpIterVarCallback	Remove SlpIterVar callback.
	RemoveSlpIterVarCallbacks	Remove all SlpIterVar callbacks.
	RemoveSlpPreUpdateLinearizationCallback	Remove SlpPreUpdateLinearization callback.
	RemoveSlpPreUpdateLinearizationCallbacks	Remove all SlpPreUpdateLinearization callbacks.
	RemoveUserSolNotifyCallback	Remove UserSolNotify callback.
	RemoveUserSolNotifyCallbacks	Remove all UserSolNotify callbacks.
	RepairInfeas(Char, Char, Char, Double, Double, Double, Double, Double)	Convenience wrapper for RepairInfeas(int,char,char,char,double,double,double,double,double) that returns the output argument.
	RepairInfeas(Int32, Char, Char, Char, Double, Double, Double, Double, Double)	Provides a simplified interface for repairWeightedInfeas.
	RepairWeightedInfeas(Double, Double, Double, Double, Char, Double, String)	Convenience wrapper for RepairWeightedInfeas(int,double[],double[],double[],double[],char,double,string) that returns the output argument.
	RepairWeightedInfeas(Int32, Double, Double, Double, Double, Char, Double, String)	By relaxing a set of selected constraints and bounds of an infeasible problem, it attempts to identify a 'solution' that violates the selected set of constraints and bounds minimally, while satisfying all other constraints and bounds. Among such solution candidates, it selects one that is optimal regarding the original objective function. For the console version, see REPAIRINFEAS.
	RepairWeightedInfeasBounds(Double, Double, Double, Double, Double, Double, Double, Double, Char, Double, String)	Convenience wrapper for RepairWeightedInfeasBounds(int,double[],double[],double[],double[],double[],double[],double[],double[],char,double,string) that returns the output argument.
	RepairWeightedInfeasBounds(Int32, Double, Double, Double, Double, Double, Double, Double, Double, Char, Double, String)	An extended version of repairWeightedInfeas that allows for bounding the level of relaxation allowed.
	Restore()	Restores the Optimizer's data structures from a file created by save ( SAVE). Optimization may then recommence from the point at which the file was created.
	Restore(String)	Restores the Optimizer's data structures from a file created by save ( SAVE). Optimization may then recommence from the point at which the file was created.
	Restore(String, String)	Restores the Optimizer's data structures from a file created by save ( SAVE). Optimization may then recommence from the point at which the file was created.
	RHSsa	Returns upper and lower sensitivity ranges for specified right hand side (RHS) function coefficients. If the RHS coefficients are varied within these ranges the current basis remains optimal and the reduced costs remain valid.
	RowTypeToArray	Convert a column type array to an array of low-level type indicators.
	Save	Saves the current data structures, i.e. matrices, control settings and problem attribute settings to file and terminates the run so that optimization can be resumed later.
	SaveAs	Saves the current data structures, i.e. matrices, control settings and problem attribute settings to file and terminates the run so that optimization can be resumed later.
	Scale	Re-scales the current matrix.
	SetDblControl	Sets the value of a given double control parameter.
	SetDefaultControl	Set a default control value.
	SetDefaults	Sets all controls to their default values. Must be called before the problem is read or loaded by readProb, loadMIP, loadLP, loadMIQP, loadQP.
	SetIndicator	Add a single indicator constraint.
	SetIndicators	Specifies that a set of rows in the matrix will be treated as indicator constraints during a tree search. An indicator constraint is made of a condition and a constraint. The condition is of the type "bin = value", where bin is a binary variable and value is either 0 or 1. The constraint is any matrix row (may be linear, quadratic or general nonlinear). During tree search, a row configured as an indicator constraint is enforced only when condition holds, that is only if the indicator variable bin has the specified value. Note that every row may only get assigned a single indicator variable and term. If a row needs to be activated by multiple different terms, the row needs to be duplicated so that each term can be assigned to a distinct row. If the indicator variable should be changed, the old term needs to be deleted first (by calling delIndicators or by calling this function with a comps argument of 0) before assigning a new one.
	SetIntControl	Sets the value of a given integer control parameter.
	SetLogFile	This directs all Optimizer output to a log file.
	SetLongControl	Sets the value of a given integer control parameter.
	SetMessageStatus	Manages suppression of messages.
	SetObjDblControl	Sets the value of a given double control parameter associated with an objective. These parameters control how the objective is treated during multi-objective optimization.
	SetObjective(Int32, Double)	Set objective to a linear function. Any previously set objective will be cleared.
	SetObjective(Int32, Double, ObjSense)	Set objective to a linear function. Any previously set objective will be cleared.
	SetObjIntControl	Sets the value of a given integer control parameter associated with an objective. These parameters control how the objective is treated during multi-objective optimization.
	SetProbName	Sets the current default problem name. This command is rarely used.
	SetStrControl	Used to set the value of a given string control parameter.
	SlpAddCoefs	Add non-linear coefficients to the SLP problem. For a simpler version of this function see XSLPaddformulas.
	SlpCascadeOrder	Establish a re-calculation sequence for SLP variables with determining rows.
	SlpCascadeSol	Re-calculate consistent values for SLP variables based on the current values of the remaining variables.
	SlpChgCascadeNLimit	Set a variable specific cascade iteration limit
	SlpChgCCoef	Obsolete. Add or change a single matrix coefficient using a character string for the formula. For a simpler version of this function see nlpChgFormulaStr.
	SlpChgCoef	Add or change a single matrix coefficient using a parsed or unparsed formula. For a simpler version of this function see XSLPchgformula.
	SlpChgCoefStr	Add or change a single matrix coefficient using a character string for the formula. For a simpler version of this function see nlpChgFormulaStr.
	SlpChgDeltaType	Changes the type of the delta assigned to a nonlinear variable
	SlpChgRowStatus(Int32)	Convenience wrapper for SlpChgRowStatus(int,int) that returns the output argument.
	SlpChgRowStatus(Int32, Int32)	Change the status setting of a constraint
	SlpChgRowWt	Set or change the initial penalty error weight for a row
	SlpConstruct	Create the full augmented SLP matrix and data structures, ready for optimization
	SlpDelCoefs(Int32, Int32)	Convenience wrapper for SlpDelCoefs(int, int[], int[]).
	SlpDelCoefs(Int32, Int32, Int32)	Delete coefficients from the current problem. For a simpler version of this function see XSLPdelformulas.
	SlpEvaluateCoef(Int32, Int32)	Convenience wrapper for SlpEvaluateCoef(int,int,double) that returns the output argument.
	SlpEvaluateCoef(Int32, Int32, Double)	Evaluate a coefficient using the current values of the variables
	SlpFixPenalties	Fixe the values of the error vectors
	SlpGetCCoef	Obsolete. Retrieve a single matrix coefficient as a formula in a character string. For a simpler version of this function see nlpGetFormulaStr.
	SlpGetCoefFormula	Retrieve a single matrix coefficient as a formula split into tokens. For a simpler version of this function see XSLPchgformula.
	SlpGetCoefs	Retrieve the list of positions of the nonlinear coefficients in the problem. For a simpler version of this function see XSLPgetformularows.
	SlpGetCoefStr	Retrieve a single matrix coefficient as a formula in a character string. For a simpler version of this function see nlpGetFormulaStr.
	SlpGetRowStatus	Retrieve the status setting of a constraint
	SlpGetRowWT(Int32)	Convenience wrapper for SlpGetRowWT(int,double) that returns the output argument.
	SlpGetRowWT(Int32, Double)	Get the initial penalty error weight for a row
	SlpLoadCoefs	Load non-linear coefficients into the SLP problem. For a simpler version of this function see XSLPloadformulas.
	SlpReInitialize	Reset the SLP problem to match a just augmented system
	SlpSetDetRow(Int32, Int32)	Convenience wrapper for SlpSetDetRow(int, int[], int[]).
	SlpSetDetRow(Int32, Int32, Int32)	Set the determining row of a variable
	SlpUnConstruct	Removes the augmentation and returns the problem to its pre-linearization state
	SlpUpdateLinearization	Updates the current linearization
	SparseBTran	Post-multiplies a (row) vector provided by the user by the inverse of the current matrix. Sparse version of bTran.
	SparseFTran	Pre-multiplies a (column) vector provided by the user by the inverse of the current matrix. Sparse version of fTran.
	StoreCuts(Int32, Int32, Int32, Char, Double, Int32, Cut, Int32, Double)	Stores cuts into the cut pool, but does not apply them to the current node. These cuts must be explicitly loaded into the matrix using loadCuts before they become active.
	StoreCuts(Int32, Int32, Int32, Char, Double, Int64, Cut, Int32, Double)	Stores cuts into the cut pool, but does not apply them to the current node. These cuts must be explicitly loaded into the matrix using loadCuts before they become active.
	StrongBranch	Performs strong branching iterations on all specified bound changes. For each candidate bound change, strongBranch performs dual simplex iterations starting from the current optimal solution of the base LP, and returns both the status and objective value reached after these iterations.
	StrongBranchCB	Performs strong branching iterations on all specified bound changes. For each candidate bound change, strongBranchCB performs dual simplex iterations starting from the current optimal solution of the base LP, and returns both the status and objective value reached after these iterations.
	ToString	Returns a String that represents the current Object. (Inherited from Object.)
	Tune	This function begins a tuner session for the current problem. The tuner will solve the problem multiple times while evaluating a list of control settings and promising combinations of them. When finished, the tuner will select and set the best control setting on the problem. Note that the direction of optimization is given by OBJSENSE.
	TuneProbSetFile	This function begins a tuner session for a set of problems. The tuner will solve the problems multiple times while evaluating a list of control settings and promising combinations of them. When finished, the tuner will select and set the best control setting on the problems.
	TunerReadMethod	This function loads a user defined tuner method from the given file.
	TunerWriteMethod	This function writes the current tuner method to a given file or prints it to the console.
	UnloadProb	Unloads and frees all memory associated with the current problem. It also invalidates the current problem (as opposed to reading in an empty problem).
	VarArray(Char, Int32, Double, Double, Func<Int32, String>)	Create an array of variables with the same type. All the variables created by this function have the same types and bounds.
	VarArray(Char, Int32, Double, Double, String)	Create an array of variables with the same type.
	VarArray(Char, Int32, Func<Int32, Double>, Func<Int32, Double>, Func<Int32, String>)	Create an array of variables with the same type.
	VarArray<T>(Char, ICollection<T>, Double, Double, Func<T, String>)	Create an array of variables with the same type. All the variables created by this function have the same types and bounds. The function will create one variable for each of the objects listed in objs.
	VarArray<T>(Char, ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>)	Create an array of variables with the same type. The function will create one variable for each of the objects listed in objs.
	VarMap<T>(Char, ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>)	Create a map of variables that all have the same type. The function creates a new variable for each object in objs. It returns a hash map in which each object in objs maps to the index of the variable that was created for it.
	VarMap<T>(Char, ICollection<T>, Func<T, Double>, Func<T, Double>, Func<T, String>, IDictionary<T, Int32>)	Create a map of variables that all have the same type. The function creates a new variable for each object in objs. For each object o it puts the Pair (o, idx) into map where idx is the index of the variable that was created for o.
	WriteBasis()	Writes the current basis to a file for later input into the Optimizer.
	WriteBasis(String)	Writes the current basis to a file for later input into the Optimizer.
	WriteBasis(String, String)	Writes the current basis to a file for later input into the Optimizer.
	WriteBinSol()	Writes the current MIP or LP solution to a binary solution file for later input into the Optimizer.
	WriteBinSol(String)	Writes the current MIP or LP solution to a binary solution file for later input into the Optimizer.
	WriteBinSol(String, String)	Writes the current MIP or LP solution to a binary solution file for later input into the Optimizer.
	WriteDirs()	Writes the tree search directives from the current problem to a directives file.
	WriteDirs(String)	Writes the tree search directives from the current problem to a directives file.
	WriteIIS(Int32, String, Int32)	Writes an LP/MPS/CSV file containing a given Irreducible Infeasible Set (IIS). If 0 is passed as the IIS number parameter, the initial infeasible subproblem is written.
	WriteIIS(Int32, String, Int32, String)	Writes an LP/MPS/CSV file containing a given Irreducible Infeasible Set (IIS). If 0 is passed as the IIS number parameter, the initial infeasible subproblem is written.
	WriteProb()	Writes the current problem to an MPS or LP file.
	WriteProb(String)	Writes the current problem to an MPS or LP file.
	WriteProb(String, String)	Writes the current problem to an MPS or LP file.
	WritePrtSol()	Writes the current solution to a fixed format ASCII file, problem_name .prt.
	WritePrtSol(String)	Writes the current solution to a fixed format ASCII file, problem_name .prt.
	WritePrtSol(String, String)	Writes the current solution to a fixed format ASCII file, problem_name .prt.
	WriteSlxSol()	Creates an ASCII solution file ( .slx) using a similar format to MPS files. These files can be read back into the Optimizer using the readSlxSol function.
	WriteSlxSol(String)	Creates an ASCII solution file ( .slx) using a similar format to MPS files. These files can be read back into the Optimizer using the readSlxSol function.
	WriteSlxSol(String, String)	Creates an ASCII solution file ( .slx) using a similar format to MPS files. These files can be read back into the Optimizer using the readSlxSol function.
	WriteSol()	Writes the current solution to a CSV format ASCII file, problem_name .asc (and .hdr).
	WriteSol(String)	Writes the current solution to a CSV format ASCII file, problem_name .asc (and .hdr).
	WriteSol(String, String)	Writes the current solution to a CSV format ASCII file, problem_name .asc (and .hdr).

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Fields

	Name	Description
	cannotAddConstraints	Error message in case we attempt to create rows with Variables where this is not possible.
	cannotAddPWLs	Error message in case we attempt to create rows with Variables where this is not possible.
	cannotAddSets	Error message in case we attempt to create sets where this is not possible.
	cannotAddVariables	Error message in case we attempt to create variables where this is not possible.
	EQ	Constraint sense for == constraints.
	GEQ	Constraint sense for >= constraints.
	LEQ	Constraint sense for <= constraints.

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Reference

Optimizer Namespace

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XPRSprob Class

Reference