Constraint Optimal Selection Techniques (COSTs) for nonnegative linear programming problems

摘要

We describe an active-set, cutting-plane approach called Constraint Optimal Selection Techniques (COSTs) and develop an efficient new COST for solving nonnegative linear programming problems. We give a geometric interpretation of the new selection rule and provide computational comparisons of the new COST with existing linear programming algorithms for some large-scale sample problems.