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20182022
most citedA Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization

24 citations · 63 across the 8 of their papers we have counts for

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5 papers · 1 filter

math.OC20215 cited

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…

math.OC202024 cited

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…

math.OC2020

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…

math.OC2019

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…

math.OC2018

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…