From the 1 of 7 linked papers with an AI index.
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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…
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…
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…
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…
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,…