10 citations · 22 across the 8 of their papers we have counts for
8 papers
An error bound-based convergence analysis framework for a class of randomized algorithms
Zhichun Yang, Li Jiang, Tianxiang Liu +1
Classical analyses show that randomized coordinate descent (RCD) and gradient descent (GD) share the same convergence rates in terms of objective gap under convexity and specific g…
Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints
Tianxiang Liu, Ting Kei Pong, Akiko Takeda
We consider a class of sparsity-inducing optimization problems whose constraint set is regularizer-compatible, in the sense that, the constraint set becomes easy-to-project-onto af…
Convergence analysis under consistent error bounds
Tianxiang Liu, Bruno F. Lourenço
We introduce the notion of consistent error bound functions which provides a unifying framework for error bounds for multiple convex sets. This framework goes beyond the classical…
A hybrid penalty method for a class of optimization problems with multiple rank constraints
Tianxiang Liu, Ivan Markovsky, Ting Kei Pong +1
In this paper, we consider the problem of minimizing a smooth objective over multiple rank constraints on Hankel-structured matrices. This kind of problems arises in system identif…
A refined convergence analysis of pDCA with applications to simultaneous sparse recovery and outlier detection
Tianxiang Liu, Ting Kei Pong, Akiko Takeda
We consider the problem of minimizing a difference-of-convex (DC) function, which can be written as the sum of a smooth convex function with Lipschitz gradient, a proper closed con…
A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems
Tianxiang Liu, Ting Kei Pong, Akiko Takeda
We consider a class of nonconvex nonsmooth optimization problems whose objective is the sum of a smooth function and a finite number of nonnegative proper closed possibly nonsmooth…
Further properties of the forward-backward envelope with applications to difference-of-convex programming
Tianxiang Liu, Ting Kei Pong
In this paper, we further study the forward-backward envelope first introduced in [28] and [30] for problems whose objective is the sum of a proper closed convex function and a twi…
Peaceman-Rachford splitting for a class of nonconvex optimization problems
Guoyin Li, Tianxiang Liu, Ting Kei Pong
We study the applicability of the Peaceman-Rachford (PR) splitting method for solving nonconvex optimization problems. When applied to minimizing the sum of a strongly convex Lipsc…