arXiv Machine Learning June 2, 2026 By Ilay Yavlovich, Jad Agbaria, Muhamed Mhamed, Nir Weinberger, Jose Yallouz

Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts

Read the original on arXiv Machine Learning →

arXiv:2605. 09382v2 Announce Type: replace Abstract: The Linear Assignment Problem is a fundamental combinatorial optimization task where classical exact solvers ensure optimality but suffer from an $\mathcal{O}(N^{3})$ bottleneck, while recent neural approximations struggle with scalability and exactness.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 12

URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization

arXiv:2509. 23413v3 Announce Type: replace Abstract: Multi-task neural routing solvers have emerged as a promising paradigm for their ability to solve multiple vehicle routing problems (VRPs) using a single model.

By Changliang Zhou, Canhong Yu, Shunyu Yao, Xi Lin, Zhenkun Wang, Yu Zhou, Qingfu Zhang

rag fine-tuning safety

arXiv Machine Learning

Jul 27

Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates

arXiv:2607. 22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive to solve.

By Anjian Li, Ryne Beeson

arXiv AI

Jun 2

Learning to Reduce Search Space for Generalizable Neural Routing Solver

arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.

By Changliang Zhou, Xi Lin, Zhenkun Wang, Qingfu Zhang

efficiency

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 AI

Jun 12

Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast

arXiv:2505. 13102v4 Announce Type: replace-cross Abstract: Unlike conventional "black-box" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions.

By Ji Qi, Tam Thuc Do, Mingxiao Liu, Zhuoshi Pan, Yuzhe Li, Gene Cheung, H. Vicky Zhao

llms benchmarks

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

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URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization

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