Optimization

Proactive Infeasibility Prevention (PIP) framework significantly improves neural methods for solving complex Vehicle Routing Problems by proactively preventing infeasible solutions and enhancing const…

AI learns to compute Gröbner bases, solving a notorious computational algebra problem efficiently via Transformers and novel algebraic techniques.

A novel Common-Points-Picking algorithm efficiently learns stable reward allocations (expected core) in strictly convex stochastic cooperative games with unknown reward distributions, achieving high p…

Learning social welfare functions from past decisions is possible! This paper shows how to efficiently learn power mean functions, a widely used family, using both cardinal and pairwise welfare compar…

CryptoANFNet accelerates solving cryptographic problems by 50x using a novel graph neural network and ANF representation, outperforming existing methods in accuracy.

AI learns optimal taxes for congestion games, maximizing social welfare with limited feedback, via a novel algorithm.

Learn optimal LP values faster with a novel neural network method!

This paper presents novel theoretical frameworks and algorithms for learning from snapshots of discrete and continuous data streams, resolving key learnability challenges in online learning under cont…

Learn optimal transport maps with structured displacements using elastic costs and a novel bilevel loss function!

JKOnet* learns diffusion processes at unprecedented speed and accuracy by directly minimizing a simple quadratic loss function, bypassing complex bilevel optimization problems.

This research develops data-driven methods for selecting optimal cut generating functions in integer programming, providing theoretical guarantees and empirical improvements over existing techniques.

Two-parameter models can surprisingly learn high-dimensional targets with near-perfect accuracy using gradient flow, challenging the need for high-dimensional models.

Latent Neural Operator (LNO) dramatically improves solving PDEs by using a latent space, boosting accuracy and reducing computation costs.

Generalized Frank-Wolfe algorithm achieves fast last-iterate convergence for constrained monotone variational inequalities, even with noisy data.

Faster John ellipsoid computation achieved via lazy updates and fast matrix multiplication, improving efficiency and enabling low-space streaming algorithms.

This paper proposes a novel framework, the locally evolving set process, to develop faster localized iterative methods for solving large-scale graph problems, achieving significant speedup over existi…

Weighted score matching offers a consistent, efficient solution for estimating parameters in point processes, overcoming the limitations of previous methods.

In strategic settings, repeated interactions alone may not enable uninformed players to achieve optimal outcomes, highlighting the persistent impact of information asymmetry.

Cross-validation isn’t always superior; simple plug-in methods often perform equally well for estimating out-of-sample model performance, especially when considering computational costs.

IPM-LSTM accelerates nonlinear program solving by up to 70% using LSTM networks to approximate linear system solutions within the interior point method.