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Log-Driven Semantic Tuning for Mixed Integer Programming Solvers — arXiv:2611.00621

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00621
License (as archived): CC0-1.0
Archived: 2026-11-03T10:34:21.439Z
SHA-256 of the archived text: 5a8bd2788d6bf3d9e54a5f5126b486ba7d495b52dffeb6bc7b9a8a657ce74265
Stable URL of this page: https://metraipolis.com/documento/dc_844fff2ea335

How to cite

Log-Driven Semantic Tuning for Mixed Integer Programming Solvers — arXiv:2611.00621 — arXiv (en), https://arxiv.org/abs/2611.00621 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_844fff2ea335 (Metraipolis archive dc_844fff2ea335, sha256 5a8bd2788d6bf3d9e54a5f5126b486ba7d495b52dffeb6bc7b9a8a657ce74265)

BibTeX

@misc{dc844fff2ea335,
  title = {Log-Driven Semantic Tuning for Mixed Integer Programming Solvers — arXiv:2611.00621},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00621},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_844fff2ea335; sha256 5a8bd2788d6bf3d9e54a5f5126b486ba7d495b52dffeb6bc7b9a8a657ce74265},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00621. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automated configuration of mixed-integer programming (MIP) solvers largely relies on black-box optimization. Each trial requires an expensive solver run and provides only a scalar outcome, so tuning can require many such evaluations. Human experts take a different approach. They inspect solver logs, identify performance bottlenecks, and adjust the parameters that address them. We propose Log-Driven Semantic Tuning (LDST), which uses a large language model (LLM) to automate this process. LDST extracts compact diagnostics from solver logs, uses the LLM to propose sparse edits to the current best configuration based on parameter meanings and observed solver behavior, and validates each proposal before execution. A regime-adaptive objective selects the tuning signal according to the information available from a time-limited run, while a log-based completion signal estimates completion time from branch-and-bound tree growth recorded in ordinary solver logs, so that solved and unsolved runs share one time scale. On 215 instances from five benchmark sets, LDST reduces geometric-mean loss by 43.9% on easy instances and 16.7% on intermediate instances relative to the best of SMAC3, RBFOpt, and random search. In both regimes, LDST achieves a lower loss with 10 proposals than any of these baselines achieves with 50. Configurations that LDST tunes on a single instance also transfer better than baseline configurations to unseen instances of the same family.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Optimization and Control (math.OC) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.00621 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:29:48.713Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).