3 citations · 9 across the 13 of their papers we have counts for
4 papers · 1 filter
Lower Bounds for Anytime Acceleration of Gradient Descent
Chung-En Tsai, Ilyas Fatkhullin, Liang Zhang +1
Recent work suggests that the convergence rate of gradient descent (GD) in smooth convex optimization can be significantly improved by employing large stepsizes that may violate th…
Direction-Magnitude Decomposition for Low-Rank Matrix Optimization: Faster Convergence and Saddle-to-saddle Dynamics
Yudong Wei, Liang Zhang, Bingcong Li +1
Low-rank matrix optimization is often carried out via the Burer-Monteiro (BM) formulation, but choosing the factorization rank is delicate and can substantially slow optimizati…
Primal Methods for Variational Inequality Problems with Functional Constraints
Liang Zhang, Niao He, Michael Muehlebach
Variational inequality problems are recognized for their broad applications across various fields including machine learning and operations research. First-order methods have emerg…
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization
Siqi Zhang, Yifan Hu, Liang Zhang +1
This paper takes an initial step to systematically investigate the generalization bounds of algorithms for solving nonconvex-(strongly)-concave (NC-SC/NC-C) stochastic minimax opti…