Discounted Pseudocosts in MILP
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Abstract (arXiv)
In this article, we introduce the concept of discounted pseudocosts, inspired by discounted total reward in reinforcement learning, and explore their application in mixed-integer linear programming (MILP). Traditional pseudocosts estimate changes in the objective function due to variable bound changes during the branch-and-bound process. By integrating reinforcement learning concepts, we propose a novel approach incorporating a forward-looking perspective into pseudocost estimation. We present the motivation behind discounted pseudocosts and discuss how they represent the anticipated reward for branching after one level of exploration in the MILP problem space. Initial experiments on MIPLIB 2017 benchmark instances demonstrate the potential of discounted pseudocosts to enhance branching strategies and accelerate the solution process for challenging MILP problems.
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References · 8
- 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 313
- 2022Learning to branch with Tree MDPscited by 45
- 2020Reinforcement Learning for Variable Selection in a Branch and Bound Algorithmcited by 28
- 2022Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectoriescited by 19
- 2022An Improved Reinforcement Learning Algorithm for Learning to Branchcited by 14
- 2022Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branchcited by 12
- 2022Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learningcited by 2
- 2023TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimizationcited by 1
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