← Back to all news
arXiv Machine Learning By Sora Todaka, Akihiro Yamamoto, Nozomi Akashi

Recycling computational processes of dynamic programming for combinatorial optimization problems: a reservoir computing approach

Read the original on arXiv Machine Learning →

arXiv:2607. 23009v1 Announce Type: new Abstract: Reusing previously computed results is a long-standing principle for reducing computational cost, but such reuse has largely been confined to a single problem's computation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jun 2

Regularized Large Neighborhood Search

arXiv:2606. 02294v1 Announce Type: new Abstract: Operations research practitioners typically tackle NP-hard combinatorial problems using large neighborhood search (LNS), a scalable heuristic that iteratively refines a current solution by locally re-optimizing subsets of its variables.

By Germain Vivier-Ardisson, Laurent Demonet, Axel Parmentier, Mathieu Blondel
More like this →
arXiv Machine Learning
Sep 16

Learning efficient representations of complex constraints for scalable optimization

The paper introduces PolyFormer, a physics-informed machine learning framework that learns compact polytopic representations of complex constraints. By transforming constraint-induced geometry into efficient polytopic reformulations, PolyFormer reduces optimization complexity and enables the use of standard solvers. Evaluations on large‑scale resource aggregation, network‑constrained optimization, and uncertainty‑aware optimization show up to 6,400‑fold speedups and 99.87% memory savings while keeping feasibility and objective errors low.

By Yilin Wen, Yi Guo, Bo Zhao, Wei Qi, Zechun Hu, Colin Jones, Jian Sun
More like this →