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math.OC2026

Stochastic Dynamic Barrier Perturbed Gradient Methods for Nonconvex Simple Bilevel Optimization

Mohammad Mahdi Ahmadi, Jincheng Cao, Aryan Mokhtari +1

We study stochastic simple bilevel optimization with smooth, possibly nonconvex upper- and lower-level objectives accessed only through stochastic gradient oracles. A key challenge…

math.OC2026

On the Analysis of Misspecified Variational Inequalities with Nonlinear Constraints

Novel Kumar Dey, Mohammad Mahdi Ahmadi, Erfan Yazdandoost Hamedani +1

In this paper, we study a class of misspecified variational inequalities (VIs) where both the monotone operator and nonlinear convex constraints depend on an unknown parameter lear…

math.OC2025

Semi-infinite Nonconvex Constrained Min-Max Optimization

Cody Melcher, Zeinab Alizadeh, Lindsey Hiett +2

Semi-Infinite Programming (SIP) has emerged as a powerful framework for modeling problems with infinite constraints, however, its theoretical development in the context of nonconve…

math.OC2025

Linear Convergence of a Unified Primal--Dual Algorithm for Convex--Concave Saddle Point Problems with Quadratic Growth

Cody Melcher, Afrooz Jalilzadeh, Erfan Yazdandoost Hamedani

In this paper, we study saddle point (SP) problems, focusing on convex-concave optimization involving functions that satisfy either two-sided quadratic functional growth (QFG) or t…

math.OC2025

A Randomized Block-Coordinate Primal-Dual Method for Large-scale Stochastic Saddle Point Problems

Erfan Yazdandoost Hamedani, Afrooz Jalilzadeh, Necdet Serhat Aybat

We consider (stochastic) convex-concave saddle point (SP) problems with high-dimensional decision variables, arising in various applications including machine learning problems. To…

math.OC2025

Sequential QCQP for Bilevel Optimization with Line Search

Sina Sharifi, Erfan Yazdandoost Hamedani, Mahyar Fazlyab

Bilevel optimization involves a hierarchical structure where one problem is nested within another, leading to complex interdependencies between levels. We propose a single-loop, tu…