arXiv (All)
open access
Collab-Solver: Collaborative Solving Policy Learning for Mixed-Integer Linear Programming
Mixed-integer linear programming (MILP) has been a fundamental problem in combinatorial optimization. Conventional MILP solving mainly relies on carefully designed heuristics embedded in the branch-and-bound framework. Driven by the strong capabilities of neural networks, recent research is explorin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Machine learning augmented branch and bound for mixed integer linear programming.#457546
- "Recursive Preferences,"#942183
- Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification#422271
- Learning Chance-Constrained MDPs with Bellman Distributional Certificates#1105415
- AN INTELLIGENT SYSTEM FOR FORMULATING LINEAR PROGRAMS#933636
Record · ID 1035305
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive