The OPTLP Procedure
- Overview
- Getting Started
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Syntax
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Details
Data Input and Output Presolve Pricing Strategies for the Primal and Dual Simplex Solvers Warm Start for the Primal and Dual Simplex Solvers The Network Simplex Algorithm The Interior Point Algorithm Iteration Log for the Primal and Dual Simplex Solvers Iteration Log for the Network Simplex Solver Iteration Log for the Interior Point Solver ODS Tables Irreducible Infeasible Set Memory Limit PROC OPTLP Macro Variable
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Examples
- References
| Pricing Strategies for the Primal and Dual Simplex Solvers |
Several pricing strategies for the primal and dual simplex solvers are available. Pricing strategies determine which variable enters the basis at each simplex pivot. They can be controlled by specifying the PRICETYPE= option.
The primal simplex solver has the following five pricing strategies:
- PARTIAL
uses Dantzig’s most violated reduced cost rule (Dantzig; 1963). It scans a queue of decision variables and selects the variable with the most violated reduced cost as the entering variable. You can optionally specify the QUEUESIZE= option to control the length of this queue.
- FULL
uses Dantzig’s most violated reduced cost rule. It compares the reduced costs of all decision variables and selects the variable with the most violated reduced cost as the entering variable.
- DEVEX
implements the Devex pricing strategy developed by Harris (1973).
- STEEPESTEDGE
uses the steepest-edge pricing strategy developed by Forrest and Goldfarb (1992).
- HYBRID
uses a hybrid of the Devex and steepest-edge pricing strategies.
The dual simplex solver has only three pricing strategies available: FULL, DEVEX, and STEEPESTEDGE.