42 citations · 157 across the 25 of their papers we have counts for
13 papers · 1 filter
Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
Lesi Chen, Boyuan Yao, Luo Luo
This paper considers stochastic first-order algorithms for minimax optimization under Polyak--Łojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem…
Accelerating Inexact HyperGradient Descent for Bilevel Optimization
Haikuo Yang, Luo Luo, Chris Junchi Li +1
We present a method for solving general nonconvex-strongly-convex bilevel optimization problems. Our method -- the \emph{Restarted Accelerated HyperGradient Descent} (\texttt{RAHGD…
Communication Efficient Distributed Newton Method with Fast Convergence Rates
Chengchang Liu, Lesi Chen, Luo Luo +1
We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires communication c…
Symmetric Rank- Methods
Chengchang Liu, Cheng Chen, Luo Luo
This paper proposes a novel class of block quasi-Newton methods for convex optimization which we call symmetric rank- (SR-) methods. Each iteration of SR- incorporates the…
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
Lesi Chen, Jing Xu, Luo Luo
We consider the optimization problem of the form , where the component is -mean-squared Lipschitz but…
On the Complexity of Decentralized Smooth Nonconvex Finite-Sum Optimization
Luo Luo, Yunyan Bai, Lesi Chen +2
We study the decentralized optimization problem , where the local function on the -th a…
Decentralized Stochastic Variance Reduced Extragradient Method
Luo Luo, Haishan Ye
This paper studies decentralized convex-concave minimax optimization problems of the form , where is the number…
Quasi-Newton Methods for Saddle Point Problems and Beyond
Chengchang Liu, Luo Luo
This paper studies quasi-Newton methods for solving strongly-convex-strongly-concave saddle point problems (SPP). We propose greedy and random Broyden family updates for SPP, which…
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization
Luo Luo, Yujun Li, Cheng Chen
We study the smooth minimax optimization problem , where is -smooth, strongly-concave in but possibly nonconvex in…
Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization
Luo Luo, Guangzeng Xie, Tong Zhang +1
This paper considers stochastic first-order algorithms for convex-concave minimax problems of the form , where can be presented by the…
Efficient Projection-Free Algorithms for Saddle Point Problems
Cheng Chen, Luo Luo, Weinan Zhang +1
The Frank-Wolfe algorithm is a classic method for constrained optimization problems. It has recently been popular in many machine learning applications because its projection-free…
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization
Guangzeng Xie, Luo Luo, Zhihua Zhang
This paper studies the lower bound complexity for the optimization problem whose objective function is the average of individual smooth convex functions. We consider the algori…
Revisiting Sub-sampled Newton Methods
Haishan Ye, Luo Luo, Zhihua Zhang
Many machine learning models depend on solving a large scale optimization problem. Recently, sub-sampled Newton methods have emerged to attract much attention for optimization due…