26 citations · 193 across the 30 of their papers we have counts for
5 papers · 1 filter
Near-Optimal Lower Bounds for Randomized Algorithms in Exact Value Zeroth-Order Convex Optimization
Haihan Zhang, Chenheng Zhang, Zhiquan Qi +1
Whether exact scalar feedback intrinsically incurs the additional dimension paid by known zeroth-order methods remains open even for Lipschitz convex optimization. For a univer…
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points
Cong Fang, Zhouchen Lin, Tong Zhang
In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an -appr…
Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters
Huan Li, Cong Fang, Wotao Yin +1
In this paper, we study the communication and (sub)gradient computation costs in distributed optimization and give a sharp complexity analysis for the proposed distributed accelera…
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
Cong Fang, Chris Junchi Li, Zhouchen Lin +1
In this paper, we propose a new technique named \textit{Stochastic Path-Integrated Differential EstimatoR} (SPIDER), which can be used to track many deterministic quantities of int…
Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation
Cong Fang, Yameng Huang, Zhouchen Lin
Asynchronous algorithms have attracted much attention recently due to the crucial demands on solving large-scale optimization problems. However, the accelerated versions of asynchr…