31 citations · 44 across the 7 of their papers we have counts for
4 papers · 1 filter
Planning in Branch-and-Bound: Model-Based Reinforcement Learning for Exact Combinatorial Optimization
Paul Strang, Zacharie Alès, Côme Bissuel +3
Mixed-Integer Linear Programming (MILP) lies at the core of many real-world combinatorial optimization (CO) problems, traditionally solved by branch-and-bound (B&B). A key driver i…
A Markov Decision Process for Variable Selection in Branch & Bound
Paul Strang, Zacharie Alès, Côme Bissuel +3
Mixed-Integer Linear Programming (MILP) is a powerful framework used to address a wide range of NP-hard combinatorial optimization problems, often solved by Branch and Bound (B&B).…
Influence branching for learning to solve mixed-integer programs online
Paul Strang, Zacharie Alès, Côme Bissuel +3
On the occasion of the 20th Mixed Integer Program Workshop's computational competition, this work introduces a new approach for learning to solve MIPs online. Influence branching,…
Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm
Marc Etheve, Zacharie Alès, Côme Bissuel +2
Mixed integer linear programs are commonly solved by Branch and Bound algorithms. A key factor of the efficiency of the most successful commercial solvers is their fine-tuned heuri…