Andrea Lodi: "Heuristics for Mixed-Integer Optimization through a Machine Learning Lens"
Andrea Lodi
Cornell University, Nueva york, Estados Unidos
Abstract:
In this talk, we discuss how a careful use of Machine Learning concepts can have an impact in primal heuristics for Mixed-Integer Programming (MIP). More precisely, we consider two applications. First, we design a data-driven scheduler for running both diving and large-neighborhood search heuristics in SCIP, one of the most effective open-source MIP solvers. Second, we incorporate a major learning component into Local Branching, one of the most well-known primal heuristic paradigms. In both cases, computational results show solid improvements over the state of the art.
Видео Andrea Lodi: "Heuristics for Mixed-Integer Optimization through a Machine Learning Lens" канала IMUS - Instituto Matemáticas Universidad de Sevilla
Cornell University, Nueva york, Estados Unidos
Abstract:
In this talk, we discuss how a careful use of Machine Learning concepts can have an impact in primal heuristics for Mixed-Integer Programming (MIP). More precisely, we consider two applications. First, we design a data-driven scheduler for running both diving and large-neighborhood search heuristics in SCIP, one of the most effective open-source MIP solvers. Second, we incorporate a major learning component into Local Branching, one of the most well-known primal heuristic paradigms. In both cases, computational results show solid improvements over the state of the art.
Видео Andrea Lodi: "Heuristics for Mixed-Integer Optimization through a Machine Learning Lens" канала IMUS - Instituto Matemáticas Universidad de Sevilla
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2 ноября 2021 г. 20:24:46
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