首页|Study Data from Delft University of Technology Update Understanding of Machine L earning (Machine Learning Augmented Branch and Bound for Mixed Integer Linear Pr ogramming)
Study Data from Delft University of Technology Update Understanding of Machine L earning (Machine Learning Augmented Branch and Bound for Mixed Integer Linear Pr ogramming)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in Machine Lea rning. According to news reporting originating from Delft, Netherlands, by NewsR x correspondents, research stated, “Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language fo r a wide range of applications. The main engine for solving MILPs is the branch- and-bound algorithm.” Funders for this research include OPTIMAL, Netherlands Organization for Scientif ic Research (NWO), TAILOR, Horizon 2020. Our news editors obtained a quote from the research from the Delft University of Technology, “Adding to the enormous algorithmic progress in MILP solving of the past decades, in more recent years there has been an explosive development in t he use of machine learning for enhancing all main tasks involved in the branch-a nd-bound algorithm. These include primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This article presents a surve y of such approaches, addressing the vision of integration of machine learning a nd mathematical optimization as complementary technologies, and how this integra tion can benefit MILP solving. In particular, we give detailed attention to mach ine learning algorithms that automatically optimize some metric of branch-and-bo und efficiency.”
DelftNetherlandsEuropeCyborgsEme rging TechnologiesMachine LearningMathematicsDelft University of Technolog y
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