arXiv Machine Learning July 27, 2026 By Akira Kitaoka

Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs

Read the original on arXiv Machine Learning →

arXiv:2607. 22263v1 Announce Type: cross Abstract: A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it arises in many applications, including integer linear programming (ILP).

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

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 Machine Learning

Jul 21

Exploiting Low-Rank Objective Structure in Discrete Quadratic Optimization

arXiv:2602. 20376v3 Announce Type: replace-cross Abstract: We study the problem of maximizing a complex-valued quadratic form over the $K^{\text{th}}$ roots of unity.

By Ria Stevens, Fangshuo Liao, Barbara Su, Thanasis Hadjidimoulas, Jianqiang Li, Anastasios Kyrillidis

benchmarks

arXiv Machine Learning

Jul 9

Statistical inverse learning and $\ell^1$-regularization

arXiv:2607. 07468v1 Announce Type: cross Abstract: We study the recovery of sparse functions from finite, noisy, and indirect observations in the framework of statistical inverse learning.

By Abhishake Rastogi, Tatiana A. Bubba, Tapio Helin, Luca Ratti

efficiency

arXiv AI

Aug 11

Halpern Iteration Achieves $\tilde{\mathcal{O}}(\epsilon^{-1/p})$ $p$th-Order Oracle Complexity for Monotone Variational Inequalities

arXiv:2608. 08463v1 Announce Type: cross Abstract: We study second- and higher-order methods for solving smooth monotone variational inequalities (MVI).

By Lesi Chen, Xinliang Zhang, Hengyu Wang, Chengchang Liu, Yongchao Chen, Jingzhao Zhang

arXiv Machine Learning

Jun 30

Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model

arXiv:2606. 28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions.

By Andrea Montanari, Kangjie Zhou

arXiv Machine Learning

Jun 26

Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity

arXiv:2606. 27298v1 Announce Type: cross Abstract: We study the fundamental problem of learning a high-dimensional Gaussian truncated to an unknown halfspace.

By Haitong Liu, Deepak Narayanan Sridharan, David Steurer, Manuel Wiedmer

Related stories

Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates

Exploiting Low-Rank Objective Structure in Discrete Quadratic Optimization

Statistical inverse learning and $\ell^1$-regularization

Halpern Iteration Achieves $\tilde{\mathcal{O}}(\epsilon^{-1/p})$ $p$th-Order Oracle Complexity for Monotone Variational Inequalities

Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model

Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity