arXiv Machine Learning

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

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.

arXiv Machine Learning

Jul 31

AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization

arXiv:2607. 27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain.

By Shengda Gu, Kai Li, Xinyi Ke, Haobo Fu, Yifan Zhang, Jian Cheng

llms reinforcement-learning

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

arXiv AI

Jun 9

Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition

arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.

By St\'ephane Eilles-Chan Way, Hugo Percot, Quentin Cappart, Tias Guns, Louis-Martin Rousseau

benchmarks

arXiv Machine Learning

Jun 18

FORGE: Foundational Optimization Representations from Graph Embeddings

arXiv:2508. 20330v5 Announce Type: replace Abstract: Combinatorial optimization problems are ubiquitous in science and engineering.

By Zohair Shafi, Serdar Kadioglu

rag fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 21

A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization

arXiv:2406. 06629v2 Announce Type: replace Abstract: This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-objective continuous black-box optimization.

By Gjorgjina Cenikj, Ana Nikolikj, Ga\v{s}per Petelin, Niki van Stein, Carola Doerr, Tome Eftimov

benchmarks

arXiv Machine Learning

Jul 28

Learning to Optimize at Scale: A Benders Decomposition-TransfORmers Framework for Stochastic Combinatorial Optimization

arXiv:2607. 22550v1 Announce Type: cross Abstract: We propose a learning-augmented Benders decomposition framework to solve large-scale two-stage stochastic mixed-integer programs.

By Seung Jin Choi, Kimiya Jozani, Josh Cooper, Esra Buyuktahtakin Toy

llms

arXiv AI

Aug 10

Evolving Parallel Algorithm Portfolios via Potential-Aware Instance Generation with LLMs

arXiv:2608. 06808v1 Announce Type: new Abstract: The Automatic Construction of Portfolios via Large Language Models (LLM-ACP) suffers from poor generalization in practical few-shot scenarios when solving complex combinatorial optimization problems.

By Shaofeng Zhang, Shengcai Liu, Zhiyuan Wang, Ke Tang

llms benchmarks

arXiv AI

Jun 3

ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization

arXiv:2501. 17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts.

By Han Fang, Paul Weng, Yutong Ban

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Decision-Focused Continual Learning for Seaport Power-Logistics Scheduling: Generalization across Varying Tasks

arXiv:2511. 07938v3 Announce Type: replace Abstract: Power-logistics scheduling in modern seaports typically follows a predict-then-optimize pipeline.

By Chuanqing Pu, Feilong Fan, Nengling Tai, Yan Xu, Wentao Huang, Honglin Wen

efficiency

arXiv Machine Learning

Jul 22

Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes

arXiv:2607. 18632v1 Announce Type: new Abstract: Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio.

By Zhaoxuan Li, Jiale Yang, Yifei Lu, Mustafa Misir

arXiv Machine Learning

Jul 31

FunL2O: LLM-Guided Feature Function Design for Learning to Optimize

arXiv:2607. 27389v1 Announce Type: new Abstract: Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance.

By Bingheng Li, Junyang Cai, Yupeng Zhang, Bistra Dilkina, Jayant Kalagnanam, Dzung T. Phan

llms

arXiv Machine Learning

Jul 27

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression

arXiv:2607. 22238v1 Announce Type: new Abstract: Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation.

By Hirotaka Sugawara, Yujin Taguchi, Kei Minagawa, Yusuke Hiki, Takashi Morikura, Akira Funahashi

benchmarks

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