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The Linear Programming Solver

The linear programming (LP) solver in the OPTMODEL procedure enables you to solve linear programming problems. A standard linear program has the 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 solvers are available in the OPTMODEL procedure:

The simplex solvers implement the two-phase simplex method. In phase I, the solver tries to find a feasible solution. If no feasible solution is found, the LP is infeasible; otherwise, the solver enters phase II to solve the original LP. The interior point solver implements a primal-dual predictor-corrector interior point algorithm. If any of the decision variables are constrained to be integer-valued, then the relaxed version of the problem is solved.

For further details, see the SAS/OR® User's Guide: Mathematical Programming: The Linear Programming Solver.
( PDF | HTML )

Examples


Statistics and Operations Research Home Page | Mathematical Programming