From the 1 of 7 linked papers with an AI index.
7 papers
Last-Iterate Convergence of Single-Loop Stochastic Methods for Constrained Convex-Concave Minimax Problems
Taoli Zheng, Jiajin Li, Anthony Man-Cho So
The paper investigates how to guarantee convergence of the final iterate of stochastic first-order algorithms for constrained smooth convex‑concave minimax problems, proposing pert…
Nonconvex Composite Functional Constraints via First-Order Augmented Lagrangian Methods under Local Regularity
Linglingzhi Zhu, Jiajin Li
We study nonasymptotic convergence of primal-dual methods for a class of nonconvex constrained optimization problems with a convex-composite structure. In this class, both the obje…
Spurious Stationarity and Hardness Results for Bregman Proximal-Type Algorithms
He Chen, Jiajin Li, Anthony Man-Cho So
Bregman proximal-type algorithms (BPs), such as mirror descent, have become popular tools in machine learning and data science for exploiting problem structures through non-Euclide…
Unifying Distributionally Robust Optimization via Optimal Transport Theory
Jose Blanchet, Daniel Kuhn, Jiajin Li +1
In recent years, two prominent paradigms have shaped distributionally robust optimization (DRO), modeling distributional ambiguity through -divergences and Wasserstein distance…
Set Smoothness Unlocks Clarke Hyper-stationarity in Bilevel Optimization
He Chen, Jiajin Li, Anthony Man-Cho So
Solving bilevel optimization (BLO) problems to global optimality is generally intractable. A common surrogate is to compute a hyper-stationary point -- a stationary point of the hy…
Doubly Smoothed Optimistic Gradients: A Universal Approach for Smooth Minimax Problems
Taoli Zheng, Anthony Man-Cho So, Jiajin Li
Smooth minimax optimization problems play a central role in a wide range of applications, including machine learning, game theory, and operations research. However, existing algori…