The Linear Programming Solver
- Overview
- Getting Started
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Syntax
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DetailsPresolvePricing Strategies for the Primal and Dual Simplex AlgorithmsThe Network Simplex AlgorithmThe Interior Point AlgorithmIteration Log for the Primal and Dual Simplex AlgorithmsIteration Log for the Network Simplex AlgorithmIteration Log for the Interior Point AlgorithmIteration Log for the Crossover AlgorithmConcurrent LPParallel ProcessingProblem StatisticsVariable and Constraint StatusIrreducible Infeasible SetMacro Variable _OROPTMODEL_
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ExamplesDiet ProblemReoptimizing the Diet Problem Using BASIS=WARMSTARTTwo-Person Zero-Sum GameFinding an Irreducible Infeasible SetUsing the Network Simplex AlgorithmMigration to OPTMODEL: Generalized NetworksMigration to OPTMODEL: Maximum FlowMigration to OPTMODEL: Production, Inventory, DistributionMigration to OPTMODEL: Shortest Path
- References
Presolve in the simplex LP algorithms of PROC OPTMODEL uses a variety of techniques to reduce the problem size, improve numerical stability, and detect infeasibility or unboundedness (Andersen and Andersen, 1995; Gondzio, 1997). During presolve, redundant constraints and variables are identified and removed. Presolve can further reduce the problem size by substituting variables. Variable substitution is a very effective technique, but it might occasionally increase the number of nonzero entries in the constraint matrix.
In most cases, using presolve is very helpful in reducing solution times. You can enable presolve at different levels or disable it by specifying the PRESOLVER= option.