2 citations · 3 across the 4 of their papers we have counts for
6 papers · 1 filter
A distributed Douglas-Rachford splitting method for solving linear constrained multi-block weakly convex problems
Leyu Hu, Jiaxin Xie, Xingju Cai +1
In recent years, a distributed Douglas-Rachford splitting method (DDRSM) has been proposed to tackle multi-block separable convex optimization problems. This algorithm offers relat…
Extended alternating structure-adapted proximal gradient algorithm for nonconvex nonsmooth problems
Ying Gao, Chunfeng Cui, Wenxing Zhang +1
Alternating structure-adapted proximal (ASAP) gradient algorithm (M. Nikolova and P. Tan, SIAM J Optim, 29:2053-2078, 2019) has drawn much attention due to its efficiency in solvin…
A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems
Qingsong Wang, Zehui Liu, Chunfeng Cui +1
In this paper, we explore a specific optimization problem that involves the combination of a differentiable nonconvex function and a nondifferentiable function. The differentiable…
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…
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…