7 papers
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…
Dual Quaternion SE(3) Synchronization with Recovery Guarantees
Jianing Zhao, Linglingzhi Zhu, Anthony Man-Cho So
Synchronization over the special Euclidean group SE(3) aims to recover absolute poses from noisy pairwise relative transformations and is a core primitive in robotics and 3D vision…
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…
Gaussian Arimoto-Blahut Algorithm for Capacity Region Calculation of Gaussian Vector Broadcast Channels
Tian Jiao, Yanlin Geng, Anthony Man-Cho So +2
This paper is concerned with the computation of the capacity region of a continuous, Gaussian vector broadcast channel (BC) with covariance matrix constraints. Since the decision v…
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…
Network Games Induced Prior for Graph Topology Learning
Chenyue Zhang, Shangyuan Liu, Hoi-To Wai +1
Learning the graph topology of a complex network is challenging due to limited data availability and imprecise data models. A common remedy in existing works is to incorporate prio…
Shuffling Gradient Descent-Ascent with Variance Reduction for Nonconvex-Strongly Concave Smooth Minimax Problems
Xia Jiang, Linglingzhi Zhu, Anthony Man-Cho So +2
In recent years, there has been considerable interest in designing stochastic first-order algorithms to tackle finite-sum smooth minimax problems. To obtain the gradient estimates,…