The Linear Programming Solver
- Overview
- Getting Started
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Syntax
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DetailsPresolvePricing Strategies for the Primal and Dual Simplex SolversThe Network Simplex AlgorithmThe Interior Point AlgorithmIteration Log for the Primal and Dual Simplex SolversIteration Log for the Network Simplex SolverIteration Log for the Interior Point SolverIteration 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 SolverMigration to OPTMODEL: Generalized NetworksMigration to OPTMODEL: Maximum FlowMigration to OPTMODEL: Production, Inventory, DistributionMigration to OPTMODEL: Shortest Path
- References
The interior point solver implements an infeasible primal-dual predictor-corrector interior point algorithm. The following information is displayed in the iteration log:
- Iter
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indicates the iteration number
- Complement
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indicates the (absolute) complementarity
- Duality Gap
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indicates the (relative) duality gap
- Primal Infeas
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indicates the (relative) primal infeasibility measure
- Bound Infeas
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indicates the (relative) bound infeasibility measure
- Dual Infeas
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indicates the (relative) dual infeasibility measure
If the sequence of solutions converges to an optimal solution of the problem, you should see all columns in the iteration log converge to zero or very close to zero. If they do not, it can be the result of insufficient iterations being performed to reach optimality. In this case, you might need to increase the value specified in the option MAXITER= or MAXTIME=. If the complementarity and/or the duality gap do not converge, the problem might be infeasible or unbounded. If the infeasibility columns do not converge, the problem might be infeasible.