5 papers
Optimal Deterministic First-Order Oracle Complexity for Nonconvex-Concave Minimax Optimization
Siyu Pan, Taoli Zheng, Jiajin Li
We study the deterministic first-order oracle complexity of smooth nonconvex-concave minimax optimization over a bounded convex dual domain. Let denote the joint smoothness…
Last-Iterate Convergence of Single-Loop Stochastic Methods for Constrained Convex-Concave Minimax Problems
Taoli Zheng, Jiajin Li, Anthony Man-Cho So
In this paper, we study last-iterate convergence of stochastic first-order methods for constrained smooth convex--concave minimax optimization under the standard bounded-variance s…
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…
Efficient Single-Loop Stochastic Algorithms for Nonconvex-Concave Minimax Optimization
Xia Jiang, Linglingzhi Zhu, Taoli Zheng +1
Nonconvex-concave (NC-C) finite-sum minimax problems have wide applications in signal processing and machine learning tasks. Conventional stochastic gradient algorithms, which rely…
A Linearly Convergent Algorithm for Rotationally Invariant -Norm Principal Component Analysis
Taoli Zheng, Peng Wang, Anthony Man-Cho So
To do dimensionality reduction on the datasets with outliers, the -norm principal component analysis (L1-PCA) as a typical robust alternative of the conventional PCA has en…