Projected Landweber iteration for matrix completion. - Hui Zhang 050; Lizhi Cheng (J. Comput. Appl. Math., 2010)

Strongly Convex Programming for Exact Matrix Completion and Robust Principal Component Analysis - Hui Zhang 050; Jian-Feng Cai 001; Lizhi Cheng; Jubo Zhu (CoRR, 2011)

Reweighted minimization model for MR image reconstruction with split Bregman method. - Hui Zhang 050; Lizhi Cheng; Jianping Li (Sci. China Inf. Sci., 2012)

Necessary and sufficient conditions of solution uniqueness in ℓ1 minimization - Hui Zhang 050; Wotao Yin; Lizhi Cheng (CoRR, 2012)

A Fast Fixed Point Algorithm for Total Variation Deblurring and Segmentation. - Dai-Qiang Chen; Hui Zhang 050; Lizhi Cheng (J. Math. Imaging Vis., 2012)

On the Constrained Minimal Singular Values for Sparse Signal Recovery. - Hui Zhang 050; Lizhi Cheng (IEEE Signal Process. Lett., 2012)

New bounds for circulant Johnson-Lindenstrauss embeddings. - Hui Zhang 050; Lizhi Cheng (CoRR, 2013)

A dual algorithm for a class of augmented convex models. - Hui Zhang 050; Lizhi Cheng; Wotao Yin (CoRR, 2013)

Gradient methods for convex minimization: better rates under weaker conditions - Hui Zhang 050; Wotao Yin (CoRR, 2013)

One condition for all: solution uniqueness and robustness of ℓ1-synthesis and ℓ1-analysis minimizations - Hui Zhang 050; Ming Yan 006; Wotao Yin (CoRR, 2013)

Proximal linearized iteratively reweighted least squares for a class of nonconvex and nonsmooth problems. - Hui Zhang 050; Tao Sun 005; Lizhi Cheng (CoRR, 2014)

Necessary and Sufficient Conditions of Solution Uniqueness in 1-Norm Minimization. - Hui Zhang 050; Wotao Yin; Lizhi Cheng (J. Optim. Theory Appl., 2015)

Restricted strong convexity and its applications to convergence analysis of gradient-type methods in convex optimization. - Hui Zhang 050; Lizhi Cheng (Optim. Lett., 2015)

One condition for solution uniqueness and robustness of both l1-synthesis and l1-analysis minimizations. - Hui Zhang 050; Ming Yan 006; Wotao Yin (Adv. Comput. Math., 2016)

The restricted strong convexity revisited: analysis of equivalence to error bound and quadratic growth. - Hui Zhang 050 (Optim. Lett., 2017)

Projected shrinkage algorithm for box-constrained ℓ1-minimization. - Hui Zhang 050; Lizhi Cheng (Optim. Lett., 2017)

Training GANs with Centripetal Acceleration. - Wei Peng 010; Yuhong Dai; Hui Zhang 050; Lizhi Cheng (CoRR, 2019)

Nonconvex Proximal Incremental Aggregated Gradient Method with Linear Convergence. - Wei Peng 010; Hui Zhang 050; Xiaoya Zhang (J. Optim. Theory Appl., 2019)

Global complexity analysis of inexact successive quadratic approximation methods for regularized optimization under mild assumptions. - Wei Peng 010; Hui Zhang 050; Xiaoya Zhang; Lizhi Cheng (J. Glob. Optim., 2020)

New analysis of linear convergence of gradient-type methods via unifying error bound conditions. - Hui Zhang 050 (Math. Program., 2020)

Training GANs with centripetal acceleration. - Wei Peng 010; Yu-Hong Dai; Hui Zhang 050; Lizhi Cheng (Optim. Methods Softw., 2020)

Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence Under Bregman Distance Growth Conditions. - Hui Zhang 050; Yu-Hong Dai; Lei Guo 022; Wei Peng 010 (Math. Oper. Res., 2021)

Bregman iterative regularization using model functions for nonconvex nonsmooth optimization. - Haoxing Yang; Hui Zhang 050; Hongxia Wang; Lizhi Cheng (Frontiers Appl. Math. Stat., 2022)

Inertial proximal incremental aggregated gradient method with linear convergence guarantees. - Xiaoya Zhang; Wei Peng 010; Hui Zhang 050 (Math. Methods Oper. Res., 2022)

Extragradient and extrapolation methods with generalized Bregman distances for saddle point problems. - Hui Zhang 050 (Oper. Res. Lett., 2022)

Quantile-based random sparse Kaczmarz for corrupted and noisy linear systems. - Lu Zhang; Hongxia Wang; Hui Zhang 050 (Numer. Algorithms, 2025)