40 citations · 91 across the 18 of their papers we have counts for
16 papers · 1 filter
Variational Theory and Algorithms for a Class of Asymptotically Approachable Nonconvex Problems
Hanyang Li, Ying Cui
We investigate a class of composite nonconvex functions, where the outer function is the sum of univariate extended-real-valued convex functions and the inner function is the limit…
The Minimization of Piecewise Functions: Pseudo Stationarity
Ying Cui, Junyi Liu, Jong-Shi Pang
There are many significant applied contexts that require the solution of discontinuous optimization problems in finite dimensions. Yet these problems are very difficult, both compu…
A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse
Hanyang Li, Ying Cui
In this paper, we have studied a decomposition method for solving a class of nonconvex two-stage stochastic programs, where both the objective and constraints of the second-stage p…
Nonconvex and Nonsmooth Approaches for Affine Chance-Constrained Stochastic Programs
Ying Cui, Junyi Liu, Jong-Shi Pang
Chance-constrained programs (CCPs) constitute a difficult class of stochastic programs due to its possible nondifferentiability and nonconvexity even with simple linear random func…
On Degenerate Doubly Nonnegative Projection Problems
Ying Cui, Ling Liang, Defeng Sun +1
The doubly nonnegative (DNN) cone, being the set of all positive semidefinite matrices whose elements are nonnegative, is a popular approximation of the computationally intractable…
Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes
Zhengling Qi, Ying Cui, Yufeng Liu +1
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patient…
Nonsmooth Composite Matrix Optimization: Strong Regularity, Constraint Nondegeneracy and Beyond
Ying Cui, Chao Ding
The nonsmooth composite matrix optimization problem (CMatOP), in particular, the matrix norm minimization problem, is a generalization of the matrix conic programming problem with…
Computing the Best Approximation Over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone
Ying Cui, Defeng Sun, Kim-Chuan Toh
This paper introduces an efficient algorithm for computing the best approximation of a given matrix onto the intersection of linear equalities, inequalities and the doubly nonnegat…
Composite Difference-Max Programs for Modern Statistical Estimation Problems
Ying Cui, Jong-Shi Pang, Bodhisattva Sen
Many modern statistical estimation problems are defined by three major components: a statistical model that postulates the dependence of an output variable on the input features; a…
On the Finite Number of Directional Stationary Values of Piecewise Programs
Ying Cui, Jong-Shi Pang
Extending a fundamental result for (indefinite) quadratic programs, this paper shows that certain non-convex piecewise programs have only a finite number of directional stationary…
A Study of Piecewise Linear-Quadratic Programs
Ying Cui, Tsung-Hui Chang, Mingyi Hong +1
Motivated by a growing list of nontraditional statistical estimation problems of the piecewise kind, this paper provides a survey of known results supplemented with new results for…
On the R-superlinear convergence of the KKT residues generated by the augmented Lagrangian method for convex composite conic programming
Ying Cui, Defeng Sun, Kim-Chuan Toh
Due to the possible lack of primal-dual-type error bounds, the superlinear convergence for the Karush-Kuhn-Tucker (KKT) residues of the sequence generated by augmented Lagrangian m…
A complete characterization on the robust isolated calmness of the nuclear norm regularized convex optimization problems
Ying Cui, Defeng Sun
In this paper, we provide a complete characterization on the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) solution mapping for convex constrained optimization problems…
Quadratic growth conditions for convex matrix optimization problems associated with spectral functions
Ying Cui, Chao Ding, Xinyuan Zhao
In this paper, we provide two types of sufficient conditions for ensuring the quadratic growth conditions of a class of constrained convex symmetric and non-symmetric matrix optimi…
On the Asymptotic Superlinear Convergence of the Augmented Lagrangian Method for Semidefinite Programming with Multiple Solutions
Ying Cui, Defeng Sun, Kim-Chuan Toh
Solving large scale convex semidefinite programming (SDP) problems has long been a challenging task numerically. Fortunately, several powerful solvers including SDPNAL, SDPNAL+ and…
On the convergence properties of a majorized ADMM for linearly constrained convex optimization problems with coupled objective functions
Ying Cui, Xudong Li, Defeng Sun +1
In this paper, we establish the convergence properties for a majorized alternating direction method of multipliers (ADMM) for linearly constrained convex optimization problems whos…