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Constraint Optimal Selection Techniques (COSTs) for Linear Programming
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作者 goh saito H. W. Corley Jay M. Rosenberger 《American Journal of Operations Research》 2013年第1期53-64,共12页
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 mul... 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. 展开更多
关键词 LINEAR PROGRAMMING Large-Scale LINEAR PROGRAMMING CUTTING PLANES Active-Set Methods CONSTRAINT Selection COSTS
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