131 citations · 192 across the 29 of their papers we have counts for
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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…
A novel augmented Lagrangian method of multipliers for optimization with general inequality constraints
Xin-Wei Liu, Yu-Hong Dai, Ya-Kui Huang +1
We introduce a twice differentiable augmented Lagrangian for nonlinear optimization with general inequality constraints and show that a strict local minimizer of the original probl…