66 citations · 324 across the 32 of their papers we have counts for
8 papers · 1 filter
A Short Note of PAGE: Optimal Convergence Rates for Nonconvex Optimization
Zhize Li
In this note, we first recall the nonconvex problem setting and introduce the optimal PAGE algorithm (Li et al., ICML'21). Then we provide a simple and clean convergence analysis o…
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method
Zhize Li
In this paper, we propose a novel accelerated gradient method called ANITA for solving the fundamental finite-sum optimization problems. Concretely, we consider both general convex…
A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization
Zhize Li, Peter Richtárik
In this paper, we study the performance of a large family of SGD variants in the smooth nonconvex regime. To this end, we propose a generic and flexible assumption capable of accur…
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Zhize Li, Dmitry Kovalev, Xun Qian +1
Due to the high communication cost in distributed and federated learning problems, methods relying on compression of communicated messages are becoming increasingly popular. While…
A unified variance-reduced accelerated gradient method for convex optimization
Guanghui Lan, Zhize Li, Yi Zhou
We propose a novel randomized incremental gradient algorithm, namely, VAriance-Reduced Accelerated Gradient (Varag), for finite-sum optimization. Equipped with a unified step-size…
A Fast Polynomial-time Primal-Dual Projection Algorithm for Linear Programming
Zhize Li, Wei Zhang, Kees Roos
Traditionally, there are several polynomial algorithms for linear programming including the ellipsoid method, the interior point method and other variants. Recently, Chubanov [Chub…
A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization
Zhize Li, Jian Li
Anderson acceleration (or Anderson mixing) is an efficient acceleration method for fixed point iterations , e.g., gradient descent can be viewed as iteratively appl…
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Zhize Li, Jian Li
We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth finite-sum problems. In particular, the objective function is given by the summation of a differentiab…