Initializing help system before first use

maximize, minimize

Purpose
Maximize/minimize the current problem.
Synopsis
procedure maximize(alg: integer, obj: linctr)
procedure maximize(obj: linctr)
procedure maximize(alg: integer, qobj: qexp)
procedure maximize(qobj: qexp)
procedure maximize(alg: integer, nlobj: nlctr)
procedure maximize(nlobj: nlctr)
procedure maximize(alg: integer, rbobj: robustctr)
procedure maximize(rbobj: robustctr)
procedure maximize(alg: integer, objs: list, objconfs: list of objconfig)
procedure maximize(objs: list, objconfs: list of objconfig)
procedure maximize(alg: integer, objs: array(range), objconfs: array(range) of objconfig)
procedure maximize(objs: array(range), objconfs: array(range) of objconfig)
Arguments
alg 
Algorithm choice:
XPRS_BAR 
Newton-Barrier to solve LP
XPRS_DUAL 
Dual simplex
XPRS_NET 
Network solver
XPRS_LIN 
Only solve LP ignoring all discrete entities
XPRS_PRI 
Primal simplex
XPRS_ENUM 
Start a search for the n-best MIP solutions
XPRS_LPSTOP 
Stop the MIP solution process after solving the first LP
XPRS_CONT 
Continue a previously interrupted solution process
XPRS_LOCAL 
Solve the linearization of the problem ( mmxnlp only)
XPRS_COREPB 
Solve the linear part of the problem ( mmxnlp only)
XPRS_TUNE 
Enable the tuner
obj 
Objective function constraint
qobj 
Quadratic objective function (with module mmquad)
nlobj 
Non linear objective function (with module mmnl)
rbobj 
Robust objective function (with module mmrobust)
objs 
Objective functions of type linctr, nlctr or mpvar (multi-objective)
objconfs 
Objective configurations (multi-objective)
Example
The following maximizes Profit using the dual simplex algorithm and stops before the branch-and-bound search: 
declarations
 Profit:linctr
end-declarations
	  	
maximize(XPRS_DUAL+XPRS_LPSTOP, Profit)
The following minimizes MinCost using the Newton-Barrier algorithm and ignoring all discrete entities 
declarations
 MinCost:linctr
end-declarations
	  	
minimize(XPRS_BAR+XPRS_LIN, MinCost)
The following solves three objectives using a lexicographic multi-objective solve, where the first two objectives are minimized and the last is maximized: 
declarations
 Obj1, Obj2, Obj3:linctr
end-declarations
	  	
minimize([Obj1, Obj2, Obj3], [objconfig(3, 1), objconfig(2, 1), objconfig(1, -1)])
The following solves a weighted combination of a linear and a nonlinear objective, where the first objective is maximized and the second is minimized: 
declarations
 Profit:linctr
 Deviation:nlctr
end-declarations

minimize([Profit, Deviation], [objconfig("weight", 2), objconfig("weight", -1)])
Further information
1. This procedure calls the Optimizer to maximize/minimize the current problem (excluding all hidden constraints) using the given constraint(s) as objective function(s). Optionally, the algorithm to be used can be defined. By default, the branch-and-bound search is executed automatically if the problem contains any discrete entities. Where appropriate, several algorithm choice parameters may be combined (using plus signs).
2. If XPRS_LIN is specified, then the discreteness of all discrete entities is ignored, even during the presolve procedure.
3. If XPRS_LPSTOP is specified, then just the LP at the top node is solved and no Branch-and-Bound search is initiated. But the discreteness of the discrete entities is taken into account in presolving the LP at the top node. Note also that getprobstat still returns information related to the MIP problem when this option is used although only an LP solve has been executed and the solution information returned by getsol corresponds to the current LP solution. However, if the the MIP is solved to optimality during this call, the MIP optimal solution will be returned by getsol.
4. If XPRS_CONT is used after a solve has completed, the routine returns immediately without altering the current problem status.
5. If XPRS_ENUM is specified, the optimiser starts a search for the n-best MIP solutions. The maximum number of solutions to store may be specified using the XPRS_enummaxsol (default: 10). After the execution of the enumeration, the number of solutions found during the search is returned by the control parameter XPRS_enumsols. The procedure selectsol can then be used to select one of these solutions.
6. If XNLP_LOCAL is specified for a non-linear problem having been loaded using mmxnlp and which have been solved using XSLP, then the current linearization will be reoptimized.
7. If XPRS_TUNE is specified the problem will be tuned and then solved with the best control settings identified by the tuner. For a user guide about the tuner, please refer to the documentation of the Xpress Optimizer.
8. If XNLP_COREPB is specified for a non-linear problem having been loaded using mmxnlp, then only the linear part of the problem will be loaded and optimized. This is usefull for checking if the linear part of the problem is well posed.
9. Support for quadratic programming requires the module mmnl.
10. Support for general nonlinear programming requires the module mmxnlp.
11. Support for robust programming requires the module mmrobust.
12. The objs parameter must be a list or array containing objects of type linctr, nlctr or mpvar or a union of these types. See the Xpress Optimizer Reference Manual for more information about multi-objective optimization.
Related topics
postsolve, loadprob, selectsol, XPRS_maxupdc, XPRS_usechecktime.
Module
mmxprs

Parent Topic Procedures and functions

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