Xpress Knitro Control Parameters
This chapter provides a full list of the controls accepted by Xpress for setting Knitro parameters. Knitro has a great number and variety of user option settings and although it tries to choose the best settings by default, often significant performance improvements can be realized by choosing some non-default option settings.
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Indicates which algorithm to use to solve the problem
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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.
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Specifies whether special emphasis is placed on getting and staying feasible in the interior-point algorithms.
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Specifies the tolerance in equation that determines whether Knitro will force subsequent iterates to remain feasible.
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Specifies the initial value for the barrier parameter : used with the barrier algorithms. This option has no effect on the Active Set algorithm.
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Indicates whether an initial point strategy is used with barrier algorithms.
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Indicates the maximum allowable number of backtracks during the linesearch of the Interior/Direct algorithm before reverting to a CG step.
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Specifies the maximum number of crossover iterations before termination.
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Indicates the maximum number of refactorizations of the KKT system per iteration of the Interior/Direct algorithm before reverting to a CG step.
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Indicates which strategy to use for modifying the barrier parameter mu in the barrier algorithms.
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Indicates whether a penalty approach is applied to the constraints.
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Indicates which penalty parameter strategy to use for determining whether or not to accept a trial iterate.
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Indicates whether or not the barrier algorithms will allow switching from an optimality phase to a pure feasibility phase.
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Specifies the initial trust region radius scaling factor used to determine the initial trust region size.
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Specifies the final relative stopping tolerance for the feasibility error.
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Specifies the final absolute stopping tolerance for the feasibility error.
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Specifies how to compute the gradients of the objective and constraint functions.
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Specifies how to compute the (approximate) Hessian of the Lagrangian.
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Indicates whether or not to enforce satisfaction of simple variable bounds throughout the optimization.
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Specifies the (relative) tolerance used for declaring infeasibility of a model.
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Specifies the number of limited memory pairs stored when approximating the Hessian using the limited-memory quasi-Newton BFGS option.
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Specifies the number of limited memory pairs stored when approximating the Hessian using the limited-memory quasi-Newton BFGS option.
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Specifies the maximum number of iterations before termination.
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Specifies which branching rule to use for MIP branch and bound procedure.
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Specifies whether or not to branch on generalized upper bounds (GUBs).
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Specifies which MIP heuristic search approach to apply to try to find an initial integer feasible point.
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Specifies the maximum number of iterations to allow for MIP heuristic, if one is enabled.
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Specifies whether or not to add constraints to the MIP derived from logical implications.
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This value specifies the threshold for deciding whether or not a variable is determined to be an integer.
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The absolute integrality gap stop tolerance for MIP.
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The relative integrality gap stop tolerance for MIP.
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Specifies rules for adding MIP knapsack cuts.
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Specifies which algorithm to use for any linear programming (LP) subproblem solves that may occur in the MIP branch and bound procedure.
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Specifies the maximum number of nodes explored.
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Specifies the maximum number of subproblem solves allowed (0 means no limit).
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Specifies which MIP method to use.
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Specifies node printing interval for
XKTR_PARAM_MIP_OUTLEVEL when
XKTR_PARAM_MIP_OUTLEVEL > 0.
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Specifies how much MIP information to print.
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Specifies the method used to initialize pseudo-costs corresponding to variables that have not yet been branched on in the MIP method.
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Specifies which algorithm to use for the root node solve in MIP (same options as
XKTR_PARAM_ALGORITHM user option).
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Specifies the MIP rounding rule to apply.
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Specifies the MIP select rule for choosing the next node in the branch and bound tree.
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Specifies the maximum number of candidates to explore for MIP strong branching.
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Specifies the maximum number of tree levels on which to perform MIP strong branching.
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Specifies the maximum number of iterations to allow for MIP strong branching solves.
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Specifies conditions for terminating the MIP algorithm.
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Specifies the extreme limits of the objective function for purposes of determining unboundedness.
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Specifies the final relative stopping tolerance for the KKT (optimality) error.
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Specifies the final absolute stopping tolerance for the KKT (optimality) error.
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Controls the level of output produced by Knitro.
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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.
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Determines the tolerance used by the Knitro presolver to remove variables and constraints from the model.
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Performs a scaling of the objective and constraint functions based on their values at the initial point.
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Specifies whether or not to try second order corrections (SOC).
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The optimization process will terminate if the relative change in all components of the solution point estimate is less than xtol.
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