2 citations · 2 across the 5 of their papers we have counts for
6 papers
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
Daoli Zhu, Lei Zhao
Linear constrained convex programming has many practical applications, including support vector machine and machine learning portfolio problems. We propose the randomized primal-du…
Level-set Subdifferential Error Bounds and Linear Convergence of Variable Bregman Proximal Gradient Method
Daoli Zhu, Sien Deng, Minghua Li +1
In this work, we develop a level-set subdifferential error bound condition aiming towards convergence rate analysis of a variable Bregman proximal gradient (VBPG) method for a broa…
A First-Order Primal-Dual Method for Nonconvex Constrained Optimization Based On the Augmented Lagrangian
Daoli Zhu, Lei Zhao, Shuzhong Zhang
Nonlinearly constrained nonconvex and nonsmooth optimization models play an increasingly important role in machine learning, statistics and data analytics. In this paper, based on…
Linear Convergence of Variable Bregman Stochastic Coordinate Descent Method for Nonsmooth Nonconvex Optimization by Level-set Variational Analysis
Lei Zhao, Daoli Zhu
Large-scale nonconvex and nonsmooth problems have attracted considerable attention in the fields of compress sensing, big data optimization and machine learning. Exploring effectiv…
Stochastic Primal-Dual Coordinate Method with Large Step Size for Composite Optimization with Composite Cone-constraints
Daoli Zhu, Lei Zhao
We introduce a stochastic coordinate extension of the first-order primal-dual method studied by Cohen and Zhu (1984) and Zhao and Zhu (2018) to solve Composite Optimization with Co…
Stochastic Primal-Dual Coordinate Method for Nonlinear Convex Cone Programs
Daoli Zhu, Lei Zhao
Block coordinate descent (BCD) methods and their variants have been widely used in coping with large-scale nonconstrained optimization problems in many fields such as imaging proce…