The Linear Programming Solver
- Overview
- Getting Started
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Syntax
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Details
Presolve Pricing Strategies for the Primal and Dual Simplex Solvers The Network Simplex Algorithm The Interior Point Algorithm Macro Variable OROPTMODEL Iteration Log for the Primal and Dual Simplex Solvers Iteration Log for the Network Simplex Solver Iteration Log for the Interior Point Solver Problem Statistics Data Magnitude and Variable Bounds Variable and Constraint Status Irreducible Infeasible Set
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Examples
Diet Problem Reoptimizing the Diet Problem Using BASIS=WARMSTART Two-Person Zero-Sum Game Finding an Irreducible Infeasible Set Using the Network Simplex Solver Migration to OPTMODEL: Generalized Networks Migration to OPTMODEL: Maximum Flow Migration to OPTMODEL: Production, Inventory, Distribution Migration to OPTMODEL: Shortest Path
- References
| Presolve |
Presolve in the simplex LP solvers 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.