131 citations · 192 across the 29 of their papers we have counts for
30 papers
A Stochastic Implicit Proximal Point Algorithm for Solving Linearly Constrained Stochastic Minimax Problems
Kehan Zhu, Jiani Wang, Yu-Hong Dai
This paper presents a novel approach to solving large-scale minimax problems with nonsmooth regularizers. We propose a stochastic implicit proximal point algorithm with variance re…
A mechanism of three-dimensional quadratic termination for the gradient method with applications
Yakui Huang, Yu-Hong Dai, Xin-Wei Liu
Recent studies show that the two-dimensional quadratic termination property has great potential in improving performance of the gradient method. However, it is not clear whether hi…
A primal-dual majorization-minimization method for large-scale linear programs
Xin-Wei Liu, Yu-Hong Dai, Ya-Kui Huang
We present a primal-dual majorization-minimization method for solving large-scale linear programs. A smooth barrier augmented Lagrangian (SBAL) function with strict convexity for t…
The Augmented Lagrangian Method Can Approximately Solve Convex Optimization with Least Constraint Violation
Yu-Hong Dai, Liwei Zhang
There are many important practical optimization problems whose feasible regions are not known to be nonempty or not, and optimizers of the objective function with the least constra…
Stability for Constrained Minimax Optimization
Yu-Hong Dai, Liwei Zhang
Minimax optimization problems are an important class of optimization problems arising from both modern machine learning and from traditional research areas. We focus on the stabili…
Mirror frameworks for relatively Lipschitz and monotone-like variational inequalities
Hui Zhang, Yu-Hong Dai
Nonconvex-nonconcave saddle-point optimization in machine learning has triggered lots of research for studying non-monotone variational inequalities (VI). In this work, we introduc…