10 citations · 19 across the 7 of their papers we have counts for
12 papers
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…
Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations
Haishan Ye, Dachao Lin, Zhihua Zhang
In this paper, we propose the greedy and random Broyden's method for solving nonlinear equations. Specifically, the greedy method greedily selects the direction to maximize a certa…
Explicit Superlinear Convergence Rates of The SR1 Algorithm
Haishan Ye, Dachao Lin, Zhihua Zhang +1
We study the convergence rate of the famous Symmetric Rank-1 (SR1) algorithm which has wide applications in different scenarios. Although it has been extensively investigated, SR1…
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate
Haishan Ye, Tong Zhang
Due to the rapid growth of smart agents such as weakly connected computational nodes and sensors, developing decentralized algorithms that can perform computations on local agents…
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction
Haishan Ye, Wei Xiong, Tong Zhang
This paper considers the decentralized composite optimization problem. We propose a novel decentralized variance-reduction proximal-gradient algorithmic framework, called PMGT-VR,…
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices
Luo Luo, Cheng Chen, Guangzeng Xie +1
We study the streaming model for approximate matrix multiplication (AMM). We are interested in the scenario that the algorithm can only take one pass over the data with limited mem…