首页|New Machine Learning Findings from Iowa State University Described (Solving a Class of Cut-generating Linear Programs Via Machine Learning)
New Machine Learning Findings from Iowa State University Described (Solving a Class of Cut-generating Linear Programs Via Machine Learning)
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Data detailed on Machine Learning have been presented. According to news reporting from Ames, Iowa, by NewsRx journalists, research stated, "Cut-generating linear programs (CGLPs) play a key role as a separation oracle to produce valid inequalities for the feasible region of mixed-integer programs. When incorporated inside branch-and-bound, the cutting planes obtained from CGLPs help to tighten relaxations and improve dual bounds." The news correspondents obtained a quote from the research from Iowa State University, "However, running the CGLPs at the nodes of the branch-and-bound tree is computationally cumbersome due to the large number of node candidates and the lack of a priori knowledge on which nodes admit useful cutting planes. As a result, CGLPs are often avoided at default settings of branch-and-cut algorithms despite their potential impact on improving dual bounds. In this paper, we propose a novel framework based on machine learning to approximate the optimal value of a CGLP class that determines whether a cutting plane can be generated at a node of the branch-and-bound tree. Translating the CGLP as an indicator function of the objective function vector, we show that it can be approximated through conventional data classification techniques. We provide a systematic procedure to efficiently generate training data sets for the corresponding classification problem based on the CGLP structure. We conduct computational experiments on benchmark instances using classification methods such as logistic regression. These results suggest that the approximate CGLP obtained from classification can improve the solution time compared with that of conventional cutting plane methods."
AmesIowaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningIowa State University
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