Miantao Chao
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View article: Coderivative-Based Newton Methods with Wolfe Linesearch for Nonsmooth Optimization
Coderivative-Based Newton Methods with Wolfe Linesearch for Nonsmooth Optimization Open
This paper introduces and develops novel coderivative-based Newton methods with Wolfe linesearch conditions to solve various classes of problems in nonsmooth optimization. We first propose a generalized regularized Newton method with Wolfe…
View article: A descent method for nonsmooth multiobjective optimization problems on Riemannian manifolds
A descent method for nonsmooth multiobjective optimization problems on Riemannian manifolds Open
In this paper, a descent method for nonsmooth multiobjective optimization problems on complete Riemannian manifolds is proposed. The objective functions are only assumed to be locally Lipschitz continuous instead of convexity used in exist…
View article: A Framework of Inertial Regularized ADMM for Separable Nonconvex Optimization Problems
A Framework of Inertial Regularized ADMM for Separable Nonconvex Optimization Problems Open
The alternating direction method of multipliers (ADMM) is an effective algorithm for solving optimization problems with separable structures. Recently, inertial technique has been widely used in various algorithms to accelerate its converg…
View article: Convergence on a Symmetric Accelerated Stochastic ADMM with Larger Stepsizes
Convergence on a Symmetric Accelerated Stochastic ADMM with Larger Stepsizes Open
View article: A Dynamical Alternating Direction Multiplier Method for Two-Block Optimization Problems
A Dynamical Alternating Direction Multiplier Method for Two-Block Optimization Problems Open
In this paper, we propose a dynamic alternating direction method of multipliers for two-block separable optimization problems. The well-known classical ADMM can be obtained after the time discretization of the dynamical system. Under suita…
View article: A Proximal Alternating Direction Method of Multipliers with a Substitution Procedure
A Proximal Alternating Direction Method of Multipliers with a Substitution Procedure Open
In this paper, we considers the separable convex programming problem with linear constraints. Its objective function is the sum of individual blocks with nonoverlapping variables and each block consists of two functions: one is smooth con…
View article: Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure
Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure Open
The alternating direction method of multipliers (ADMM) is an effective method for solving two-block separable convex problems and its convergence is well understood. When either the involved number of blocks is more than two, or there is a…
View article: Some characterizations of error bound for non-lower semicontinuous functions
Some characterizations of error bound for non-lower semicontinuous functions Open