6 citations · 17 across the 8 of their papers we have counts for
5 papers · 1 filter
Generalized Polyak Step Size for First Order Optimization with Momentum
Xiaoyu Wang, Mikael Johansson, Tong Zhang
In machine learning applications, it is well known that carefully designed learning rate (step size) schedules can significantly improve the convergence of commonly used first-orde…
PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium
Shihong Ding, Hanze Dong, Cong Fang +2
We consider the non-convex non-concave objective function in two-player zero-sum continuous games. The existence of pure Nash equilibrium requires stringent conditions, posing a ma…
Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization
Conghui Tan, Yuqiu Qian, Shiqian Ma +1
Dual averaging-type methods are widely used in industrial machine learning applications due to their ability to promoting solution structure (e.g., sparsity) efficiently. In this p…
Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold
Shixiang Chen, Shiqian Ma, Anthony Man-Cho So +1
We consider optimization problems over the Stiefel manifold whose objective function is the summation of a smooth function and a nonsmooth function. Existing methods for solving th…
On the Duality Gap Convergence of ADMM Methods
Da Tang, Tong Zhang
This paper provides a duality gap convergence analysis for the standard ADMM as well as a linearized version of ADMM. It is shown that under appropriate conditions, both methods ac…