2 citations · 11 across the 16 of their papers we have counts for
16 papers
On subspace-constrained preconditioning for randomized iterative methods
Yonghan Sun, Hou-Duo Qi, Deren Han +1
In this paper, we further investigate and refine the subspace-constrained preconditioning technique to enhance the theoretical and numerical convergence properties of randomized it…
Randomized conjugate gradient least squares
Yun Zeng, Jian-Feng Cai, Deren Han +1
We develop a novel randomized conjugate gradient least squares (RCGLS) method for solving least-squares problems, in which iterative sketching is employed at each step to reduce th…
Linear convergence of Gearhart-Koshy accelerated Kaczmarz methods for tensor linear systems
Yijie Wang, Yonghan Sun, Deren Han +1
The generalized Gearhart-Koshy acceleration is a recent exact affine search technique designed for the method of cyclic projections onto hyperplanes, i.e., the Kaczmarz method. How…
On greedy multi-step inertial randomized Kaczmarz method for solving linear systems
Yansheng Su, Deren Han, Yun Zeng +1
The multi-step inertial randomized Kaczmarz (MIRK) method is an iterative method for solving large-scale linear systems. In this paper, we enhance the MIRK method by incorporating…
Stochastic dual coordinate descent with adaptive heavy ball momentum for linearly constrained convex optimization
Yun Zeng, Deren Han, Yansheng Su +1
The problem of finding a solution to the linear system with certain minimization properties arises in numerous scientific and engineering areas. In the era of big data, th…
On the convergence analysis of the greedy randomized Kaczmarz method
Yansheng Su, Deren Han, Yun Zeng +1
In this paper, we analyze the greedy randomized Kaczmarz (GRK) method proposed in Bai and Wu (SIAM J. Sci. Comput., 40(1):A592--A606, 2018) for solving linear systems. We develop m…
On adaptive stochastic heavy ball momentum for solving linear systems
Yun Zeng, Deren Han, Yansheng Su +1
The stochastic heavy ball momentum (SHBM) method has gained considerable popularity as a scalable approach for solving large-scale optimization problems. However, one limitation of…
Randomized Kaczmarz method with adaptive stepsizes for inconsistent linear systems
Yun Zeng, Deren Han, Yansheng Su +1
We investigate the randomized Kaczmarz method that adaptively updates the stepsize using readily available information for solving inconsistent linear systems. A novel geometric in…
A greedy randomized average block projection method for linear feasibility problems
Lin Zhu, Yuan Lei, Jiaxin Xie
The randomized projection (RP) method is a simple iterative scheme for solving linear feasibility problems and has recently gained popularity due to its speed and low memory requir…
On pseudoinverse-free randomized methods for linear systems: Unified framework and acceleration
Deren Han, Jiaxin Xie
We present a new framework for the analysis and design of randomized algorithms for solving various types of linear systems, including consistent or inconsistent, full rank or rank…
Randomized Douglas-Rachford methods for linear systems: Improved accuracy and efficiency
Deren Han, Yansheng Su, Jiaxin Xie
The Douglas-Rachford (DR) method is a widely used method for finding a point in the intersection of two closed convex sets (feasibility problem). However, the method converges weak…
Graph Sparsification by Universal Greedy Algorithms
Ming-Jun Lai, Jiaxin Xie, Zhiqiang Xu
Graph sparsification is to approximate an arbitrary graph by a sparse graph and is useful in many applications, such as simplification of social networks, least squares problems, n…
Upper and Lower bounds for matrix discrepancy
Jiaxin Xie, Zhiqiang Xu, Ziheng Zhu
The aim of this paper is to study the matrix discrepancy problem. Assume that are independent scalar random variables with finite support and $\mathbf{u}_1,\ldots,…
A note on restricted invertibility with weighted columns
Jiaxin Xie
The restricted invertibility theorem was originally introduced by Bourgain and Tzafriri in and has been considered as one of the most celebrated theorems in geometry and ana…
A symmetric alternating minimization algorithm for total variation minimization
Yuan Lei, Jiaxin Xie
In this paper, we propose a novel symmetric alternating minimization algorithm to solve a broad class of total variation (TV) regularization problems. Unlike the usual …
Subset Selection for Matrices with Fixed Blocks
Jiaxin Xie, Zhiqiang Xu
Subset selection for matrices is the task of extracting a column sub-matrix from a given matrix with such that the pseudoinverse of the sampled m…