316 citations · 760 across the 38 of their papers we have counts for
38 papers
A Gaussian smoothing-based zeroth-order method for Goldstein second-order stationarity
Ming Lei, Ting Kei Pong, Man-Chung Yue +2
We introduce a new generalized Hessian, called the Goldstein second-order -subdifferential, and an associated notion of -second-order stationary point for continuou…
A smoothing extended sequential quadratic method for difference-of-convex optimization over a convex composite inequality constraint
Jiefeng Xu, Ting Kei Pong, Yongle Zhang
We consider the problem of minimizing a difference-of-convex objective over a convex composite inequality constraint and a compact convex set constraint. To solve this problem, we…
A conditional-gradient-based single-loop augmented Lagrangian method for inequality constrained optimization
Xiaozhou Wang, Ting Kei Pong, Zev Woodstock
We consider the problem of minimizing the sum of a Lipschitz differentiable convex function and a proper closed convex function that admits efficient linear minimization or…
Complexity and convergence analysis of a single-loop SDCAM for Lipschitz composite optimization and beyond
Hao Zhang, Naoki Marumo, Ting Kei Pong +1
We develop and analyze a single-loop algorithm for minimizing the sum of a Lipschitz differentiable function , a prox-friendly proper closed function (with a closed domain o…
A smoothing moving balls approximation method for a class of conic-constrained difference-of-convex optimization problems
Jiefeng Xu, Ting Kei Pong, Nung-sing Sze
In this paper, we consider the problem of minimizing a difference-of-convex objective over a nonlinear conic constraint, where the cone is closed, convex, pointed and has a nonempt…
Burer-Monteiro factorizability of nuclear norm regularized optimization
Wenqing Ouyang, Ting Kei Pong, Man-Chung Yue
This paper studies the relationship between the nuclear norm-regularized minimization problem, which minimizes the sum of a function and a positive multiple of the nuclea…
Change Point Detection in Precision Matrices with D-trace Loss
Ying Lin, Benjamin Poignard, Ting Kei Pong +1
We consider the problem of estimating a time-varying sparse precision matrix, which is assumed to evolve in a piecewise constant manner. Building upon the Group Fused LASSO and LAS…
A single-loop proximal-conditional-gradient penalty method
Hao Zhang, Liaoyuan Zeng, Ting Kei Pong
We consider the problem of minimizing a convex separable objective (as a separable sum of two proper closed convex functions and ) over a linear coupling constraint. We assu…
Subdifferentially polynomially bounded functions and Gaussian smoothing-based zeroth-order optimization
Ming Lei, Ting Kei Pong, Shuqin Sun +1
We study the class of subdifferentially polynomially bounded (SPB) functions, which is a rich class of locally Lipschitz functions that encompasses all Lipschitz functions, all gra…
Tight error bounds for log-determinant cones without constraint qualifications
Ying Lin, Scott B. Lindstrom, Bruno F. Lourenço +1
In this paper, without requiring any constraint qualifications, we establish tight error bounds for the log-determinant cone, which is the closure of the hypograph of the perspecti…
Kurdyka-Łojasiewicz exponent via Hadamard parametrization
Wenqing Ouyang, Yuncheng Liu, Ting Kei Pong +1
We consider a class of -regularized optimization problems and the associated smooth "over-parameterized" optimization problems built upon the Hadamard parametrization, or e…
An extended sequential quadratic method with extrapolation
Yongle Zhang, Ting Kei Pong, Shiqi Xu
We revisit and adapt the extended sequential quadratic method (ESQM) in [3] for solving a class of difference-of-convex optimization problems whose constraints are defined as the i…
Convergence rate analysis of a Dykstra-type projection algorithm
Xiaozhou Wang, Ting Kei Pong
Given closed convex sets , , and some nonzero linear maps , , of suitable dimensions, the multi-set split feasibility problem aims at…
Generalized power cones: optimal error bounds and automorphisms
Ying Lin, Scott B. Lindstrom, Bruno F. Lourenço +1
Error bounds are a requisite for trusting or distrusting solutions in an informed way. Until recently, provable error bounds in the absence of constraint qualifications were unatta…
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…
Doubly iteratively reweighted algorithm for constrained compressed sensing models
Shuqin Sun, Ting Kei Pong
We propose a new algorithmic framework for constrained compressed sensing models that admit nonconvex sparsity-inducing regularizers including the log-penalty function as objective…
Frank-Wolfe-type methods for a class of nonconvex inequality-constrained problems
Liaoyuan Zeng, Yongle Zhang, Guoyin Li +2
The Frank-Wolfe (FW) method, which implements efficient linear oracles that minimize linear approximations of the objective function over a fixed compact convex set, has recently r…
Optimal error bounds in the absence of constraint qualifications with applications to the -cones and beyond
Scott B. Lindstrom, Bruno F. Lourenço, Ting Kei Pong
We prove tight Hölderian error bounds for all -cones. Surprisingly, the exponents differ in several ways from those that have been previously conjectured; moreover, they illumin…
Retraction-based first-order feasible methods for difference-of-convex programs with smooth inequality and simple geometric constraints
Yongle Zhang, Guoyin Li, Ting Kei Pong +1
In this paper, we propose first-order feasible methods for difference-of-convex (DC) programs with smooth inequality and simple geometric constraints. Our strategy for maintaining…
Error bounds, facial residual functions and applications to the exponential cone
Scott B. Lindstrom, Bruno F. Lourenço, Ting Kei Pong
We construct a general framework for deriving error bounds for conic feasibility problems. In particular, our approach allows one to work with cones that fail to be amenable or eve…
Analysis and algorithms for some compressed sensing models based on L1/L2 minimization
Liaoyuan Zeng, Peiran Yu, Ting Kei Pong
Recently, in a series of papers [32,38,39,41], the ratio of and norms was proposed as a sparsity inducing function for noiseless compressed sensing. In this paper…
Convergence rate analysis of a sequential convex programming method with line search for a class of constrained difference-of-convex optimization problems
Peiran Yu, Ting Kei Pong, Zhaosong Lu
In this paper, we study the sequential convex programming method with monotone line search (SCP) in [46] for a class of difference-of-convex (DC) optimization problems with…
Iteratively reweighted algorithms with extrapolation
Peiran Yu, Ting Kei Pong
Iteratively reweighted algorithm is a popular algorithm for solving a large class of optimization problems whose objective is the sum of a Lipschitz differentiable loss fu…
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…