The LP Procedure
- Overview
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Getting Started
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Syntax
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DetailsMissing ValuesDense Data Input FormatSparse Data Input FormatConverting Any PROC LP Format to an MPS-Format SAS Data SetConverting Standard MPS Format to Sparse FormatThe Reduced Costs, Dual Activities, and Current TableauMacro Variable _ORLP_PricingScalingPreprocessingInteger ProgrammingSensitivity AnalysisRange AnalysisParametric ProgrammingInteractive FacilitiesMemory ManagementOutput Data SetsInput Data SetsDisplayed OutputODS Table and Variable NamesMemory Limit
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ExamplesAn Oil Blending ProblemA Sparse View of the Oil Blending ProblemSensitivity Analysis: Changes in Objective CoefficientsAdditional Sensitivity AnalysisPrice Parametric Programming for the Oil Blending ProblemSpecial Ordered Sets and the Oil Blending ProblemGoal-Programming a Product Mix ProblemA Simple Integer ProgramAn Infeasible ProblemRestarting an Integer ProgramAlternative Search of the Branch-and-Bound TreeAn Assignment ProblemA Scheduling ProblemA Multicommodity Transshipment Problem with Fixed ChargesConverting to an MPS-Format SAS Data SetMigration to OPTMODEL: AssignmentMigration to OPTMODEL: Multicommodity Transshipment
- References
With the preprocessing option, you can identify redundant and infeasible constraints, improve lower and upper bounds of variables, fix variable values and improve coefficients and RHS values before solving a problem. Preprocessing can be applied to LP, IP and MIP problems. For an LP problem, it may significantly reduce the problem size. For an IP or MIP problem, it can often reduce the gap between the optimal solution and the solution of the relaxed problem, which could lead to a smaller search tree in the branch-and-bound algorithm. As a result, the CPU time may be reduced on many problems. Although there is no guarantee that preprocessing will always yield a faster solution, it does provide a highly effective approach to solving large and difficult problems.
Preprocessing is especially useful when the original problem causes numerical difficulties to PROC LP. Since preprocessing could identify redundant constraints and tighten lower and upper bounds of variables, the reformulated problem may eliminate the numerical difficulties in practice.
When a constraint is identified as redundant, its type is marked as 'FREE' in the Constraint Summary. If a variable is fixed, its type is marked as 'FIXED' in the Variables Summary. If a constraint is identified as infeasible, PROC LP stops immediately and displays the constraint name in the SAS log file. This capability sometimes gives valuable insight into the model or the formulation and helps establish if the model is reasonable and the formulation is correct.
For a large and dense problem, preprocessing may take a longer time for each iteration. To limit the number of preprocessings, use the PMAXIT= option. To stop any further preprocessings during the preprocessing stage, press the CTRL-BREAK key. PROC LP will enter phase 1 at the end of the current iteration.