The OPTLP Procedure
- Overview
- Getting Started
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Syntax
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DetailsData Input and OutputPresolvePricing Strategies for the Primal and Dual Simplex AlgorithmsWarm Start 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 ProcessingODS TablesIrreducible Infeasible SetMacro Variable _OROPTLP_
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Examples
- References
Pricing Strategies for the Primal and Dual Simplex Algorithms
Several pricing strategies for the primal and dual simplex algorithms 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 algorithm has the following five pricing strategies:
- PARTIAL
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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
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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
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implements the Devex pricing strategy developed by Harris (1973).
- STEEPESTEDGE
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uses the steepest-edge pricing strategy developed by Forrest and Goldfarb (1992).
- HYBRID
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uses a hybrid of the Devex and steepest-edge pricing strategies.
The dual simplex algorithm has only three pricing strategies available: FULL, DEVEX, and STEEPESTEDGE.