33 citations · 103 across the 18 of their papers we have counts for
18 papers
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…
Improving the generalization via coupled tensor norm regularization
Ying Gao, Yunfei Qu, Chunfeng Cui +1
In this paper, we propose a coupled tensor norm regularization that could enable the model output feature and the data input to lie in a low-dimensional manifold, which helps us to…
Understanding the convergence of the preconditioned PDHG method: a view of indefinite proximal ADMM
Yumin Ma, Xingju Cai, Bo Jiang +1
The primal-dual hybrid gradient (PDHG) algorithm is popular in solving min-max problems which are being widely used in a variety of areas. To improve the applicability and efficien…
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 fixed-time inverse-free dynamical system for solving the system of absolute value equations
Xuehua Li, Dongmei Yu, Yinong Yang +2
In this paper, an inverse-free dynamical system with fixed-time convergence is presented to solve the system of absolute value equations (AVEs). Under a mild condition, it is prove…
A dynamical system based on projection operator for solving absolute value equations associated with second-order cone
Cairong Chen, Dongmei Yu, Deren Han +1
A new equivalent reformulation of the absolute value equations associated with second-order cone (SOCAVEs) is emphasised, from which a dynamical system based on projection operator…
On finite termination of the generalized Newton method for solving absolute value equations
Jia Tang, Wenli Zheng, Cairong Chen +2
Motivated by the framework constructed by Brugnano and Casulli SIAM J. Sci. Comput. 30: 463--472, 2008, we analyze the finite termination property of the generalized Netwon m…
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…
A non-monotone smoothing Newton algorithm for solving the system of generalized absolute value equations
Cairong Chen, Dongmei Yu, Deren Han +1
The system of generalized absolute value equations (GAVE) has attracted more and more attention in the optimization community. In this paper, by introducing a smoothing function, w…
An inexact Douglas-Rachford splitting method for solving absolute value equations
Cairong Chen, Dongmei Yu, Deren Han
The last two decades witnessed the increasing of the interests on the absolute value equations (AVE) of finding such that , where $A\in \mathbb{R}^{n…
Convergence on a symmetric accelerated stochastic ADMM with larger stepsizes
Jianchao Bai, Deren Han, Hao Sun +1
In this paper, we develop a symmetric accelerated stochastic Alternating Direction Method of Multipliers (SAS-ADMM) for solving separable convex optimization problems with linear c…
An inexact framework of the Newton-based matrix splitting iterative method for the generalized absolute value equation
Dongmei Yu, Cairong Chen, Deren Han
An inexact framework of the Newton-based matrix splitting (INMS) iterative method is developed to solve the generalized absolute value equation, whose exact version was proposed by…
Optimal parameter for the SOR-like iteration method for solving the system of absolute value equations
Cairong Chen, Dongmei Yu, Deren Han
The SOR-like iteration method for solving the absolute value equations~(AVE) of finding a vector such that with is investigated. The co…
Linear Rate Convergence of the Alternating Direction Method of Multipliers for Convex Composite Quadratic and Semi-Definite Programming
Deren Han, Defeng Sun, Liwei Zhang
In this paper, we aim to provide a comprehensive analysis on the linear rate convergence of the alternating direction method of multipliers (ADMM) for solving linearly constrained…
An indefinite-proximal-based strictly contractive Peaceman-Rachford splitting method
Yan Gu, Bo Jiang, Deren Han
The Peaceman-Rachford splitting method is efficient for minimizing a convex optimization problem with a separable objective function and linear constraints. However, its convergenc…