The OPTLP Procedure

Overview: OPTLP Procedure

The OPTLP procedure provides four methods of solving linear programs (LPs). A linear program has the following formulation:

where

			is the vector of decision variables
			is the matrix of constraints
			is the vector of objective function coefficients
			is the vector of constraints’ right-hand sides (RHS)
			is the vector of lower bounds on variables
			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 18, 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" (SAS/OR User's Guide: Mathematical Programming Legacy Procedures), Chapter 3, "The INTPOINT Procedure" (SAS/OR User's Guide: Mathematical Programming Legacy Procedures), and Chapter 5, "The NETFLOW Procedure" (SAS/OR User's Guide: Mathematical Programming Legacy Procedures).

Last updated: August 11, 2020