The OPTLP procedure provides four methods of solving linear programs (LPs). A linear program has the following formulation:
where
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is the vector of decision variables |
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is the matrix of constraints |
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is the vector of objective function coefficients |
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is the vector of constraints right-hand sides (RHS) |
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is the vector of lower bounds on variables |
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is the vector of upper bounds on variables |
The following LP algorithms are available in the OPTLP procedure:
primal simplex algorithm
dual simplex algorithm
network simplex algorithm
interior point algorithm
The primal and dual simplex algorithms implement the two-phase simplex method. In phase I, the algorithm tries to find a feasible solution. If no feasible solution is found, the LP is infeasible; otherwise, the algorithm enters phase II to solve the original LP. The network simplex algorithm extracts a network substructure, solves this using network simplex, and then constructs an advanced basis to feed to either primal or dual simplex. The interior point algorithm implements a primal-dual predictor-corrector interior point algorithm.
PROC OPTLP requires a linear program to be specified using a SAS data set that adheres to the MPS format, a widely accepted format in the optimization community. For details about the MPS format see Chapter 17: The MPS-Format SAS Data Set.
You can use the MPSOUT= option to convert typical PROC LP format data sets into MPS-format SAS data sets. The option is available in the LP, INTPOINT, and NETFLOW procedures. For details about this option, see Chapter 4: The LP Procedure in SAS/OR 14.1 User's Guide: Mathematical Programming Legacy Procedures, Chapter 3: The INTPOINT Procedure in SAS/OR 14.1 User's Guide: Mathematical Programming Legacy Procedures, and Chapter 5: The NETFLOW Procedure in SAS/OR 14.1 User's Guide: Mathematical Programming Legacy Procedures.