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20182026
most citedThe Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

7 citations · 9 across the 17 of their papers we have counts for

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17 papers · 1 filter

math.OC2026

Promoting Fair Online Resource Allocation with Indivisible Units

Igor Averbakh, Hongyi Jiang, Samitha Samaranayake +2

Allocating scarce, indivisible resources to diverse groups under uncertainty is a central challenge in operations research, where efficiency-focused methods often underserve margin…

math.OC2025

Successive Fixing for Large-Scale SCUC Using First-Order Methods

Jinxin Xiong, Yanting Huang, Yingxiao Wang +4

Security-Constrained Unit Commitment is a fundamental optimization problem in power systems operations. The primary computational bottleneck arises from the need to solve large-sca…

math.OC2025

Smoothing Binary Optimization: A Primal-Dual Perspective

Wenbo Liu, Akang Wang, Dun Ma +3

Binary optimization is a powerful tool for modeling combinatorial problems, yet scalable and theoretically sound solution methods remain elusive. Conventional solvers often rely on…

math.OC2025

Solving Quadratic Programs via Deep Unrolled Douglas-Rachford Splitting

Jinxin Xiong, Xi Gao, Linxin Yang +3

Convex quadratic programs (QPs) are fundamental to numerous applications, including finance, engineering, and energy systems. Among the various methods for solving them, the Dougla…

math.OC2025

Relax-and-Cut for Temporal SCUC Decomposition

Jinxin Xiong, Linxin Yang, Yingxiao Wang +4

The Security-Constrained Unit Commitment (SCUC) problem presents formidable computational challenges due to its combinatorial complexity, large-scale network dimensions, and numero…

math.OC2025

A Learning-Based Inexact ADMM for Solving Quadratic Programs

Xi Gao, Jinxin Xiong, Linxin Yang +3

Convex quadratic programs (QPs) constitute a fundamental computational primitive across diverse domains including financial optimization, control systems, and machine learning. The…