Constraint Optimal Selection Techniques (COSTs) for Linear Programming

作     者：Goh Saito H. W. Corley Jay M. Rosenberger 

作者机构：IMSE Department The University of Texas at Arlington Arlington USA 

出 版 物：《American Journal of Operations Research》 (美国运筹学期刊（英文）)

年 卷 期：2013年第3卷第1期

页      面：53-64页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Linear Programming Large-Scale Linear Programming Cutting Planes Active-Set Methods Constraint Selection COSTs 

摘      要：We describe a new active-set, cutting-plane Constraint Optimal Selection Technique (COST) for solving general linear programming problems. We describe strategies to bound the initial problem and simultaneously add multiple constraints. We give an interpretation of the new COST’s selection rule, which considers both the depth of constraints as well as their angles from the objective function. We provide computational comparisons of the COST with existing linear programming algorithms, including other COSTs in the literature, for some large-scale problems. Finally, we discuss conclusions and future research.