Improving branch-and-bound using decision diagrams and reinforcement learning

DOI10.1007/978-3-030-78230-6_28OpenAlexW3168531812MaRDI QIDQ2117248

Yanyan Li

Publication date: 21 March 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-78230-6_28

zbMATH Keywords

branch-and-boundreinforcement learningdecision diagrams

Mathematics Subject Classification ID

Combinatorial optimization (90C27) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (3)

Improving Variable Orderings of Approximate Decision Diagrams Using Reinforcement Learning ⋮ Decision Diagrams for Discrete Optimization: A Survey of Recent Advances ⋮ Improved Peel-and-Bound: Methods for Generating Dual Bounds with Multivalued Decision Diagrams

Uses Software

• azove

Cites Work

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• Three-dimensional matching instances are rich in stable matchings
• \({\mathcal Q}\)-learning
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• Manipulating MDD Relaxations for Combinatorial Optimization
• Statistical mechanics of complex networks
• On Threshold BDDs and the Optimal Variable Ordering Problem
• Graph-Based Algorithms for Boolean Function Manipulation
• An MDD Approach to Multidimensional Bin Packing
• Multivalued Decision Diagrams for Sequencing Problems
• Benchmarking optimization software with performance profiles.

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