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math.OC2026

Scalable Dynamic Optimal Transport via Distributed Linearized ADMM

Hari Dahal, Rongjie Lai, Yangyang Xu

The paper proposes a reformulation of the dynamic optimal transport problem that admits an exact proximal mapping and combines it with a linearized ADMM algorithm to remain stable…

math.OC2026

A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization

Wei Liu, Muhammad Khan, Gabriel Mancino-Ball +1

The paper introduces a stochastic smoothing proximal gradient algorithm for solving nonconvex‑nonconcave minimization‑expectation‑maximization (minEmax) problems, providing converg…

math.OC2026

Inexact Moreau Envelope Lagrangian Method for Non-Convex Constrained Optimization under Local Error Bound Conditions on Constraint Functions

Yankun Huang, Qihang Lin, Yangyang Xu

In this paper, we investigate how structural properties of the constraint system impact the oracle complexity of smooth non-convex optimization problems with convex inequality cons…

math.OC2025

A single-loop SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization

Wei Liu, Yangyang Xu

Many real-world problems, such as those with fairness constraints, involve complex expectation constraints and large datasets, necessitating the design of efficient stochastic meth…

math.OC2025

Lower Complexity Bounds of First-order Methods for Affinely Constrained Composite Non-convex Problems

Wei Liu, Qihang Lin, Yangyang Xu

Many recent studies on first-order methods (FOMs) focus on \emph{composite non-convex non-smooth} optimization with linear and/or nonlinear function constraints. Upper (or worst-ca…

math.OC2025

A Near-optimal Method for Linearly Constrained Composite Non-convex Non-smooth Problems

Wei Liu, Qihang Lin, Yangyang Xu

We study first-order methods (FOMs) for solving \emph{composite nonconvex nonsmooth} optimization with linear constraints. Recently, the lower complexity bounds of FOMs on finding…