Online learning for scheduling MIP heuristics

DOI10.1007/978-3-031-33271-5_8arXiv2304.03755OpenAlexW4377231304MaRDI QIDQ6057253

Antonia Chmiela, Sebastian Pokutta, Ambros M. Gleixner, Pawel Lichocki

Publication date: 4 October 2023

Published in: Integration of Constraint Programming, Artificial Intelligence, and Operations Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2304.03755

zbMATH Keywords

heuristicsmixed integer programmingmachine learning

Mathematics Subject Classification ID

Combinatorial optimization (90C27) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Operations research and management science (90Bxx)

Cites Work

• A computational study of primal heuristics inside an MI(NL)P solver
• Learning when to use a decomposition
• MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library
• Reinforcement learning for variable selection in a branch and bound algorithm
• Adaptive large neighborhood search for mixed integer programming
• On learning and branching: a survey
• Measuring the impact of primal heuristics
• Conflict analysis in mixed integer programming
• An Automatic Method of Solving Discrete Programming Problems
• ParamILS: An Automatic Algorithm Configuration Framework
• From feasibility to improvement to proof: three phases of solving mixed-integer programs
• Bandit Algorithms
• Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
• Adaptive cut selection in mixed-integer linear programming

This page was built for publication: Online learning for scheduling MIP heuristics