Aspects of large-scale in-core linear programming

Abstract

Unconventional methods for matricial compression indicate that large linear programming constraint matrices may comfortably remain core-resident during optimization. Minor changes in the computational aspects of the simplex algorithm coupled with efficient inverse matrix representation show that the major portion of the inverse in product form of a basis may be embedded in the constraint matrix. A method for generating a sparse inverse matrix is presented.