Ying Cui

613 total citations
27 papers, 297 citations indexed

About

Ying Cui is a scholar working on Computational Mechanics, Computational Theory and Mathematics and Numerical Analysis. According to data from OpenAlex, Ying Cui has authored 27 papers receiving a total of 297 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computational Mechanics, 13 papers in Computational Theory and Mathematics and 10 papers in Numerical Analysis. Recurrent topics in Ying Cui's work include Sparse and Compressive Sensing Techniques (13 papers), Optimization and Variational Analysis (11 papers) and Advanced Optimization Algorithms Research (10 papers). Ying Cui is often cited by papers focused on Sparse and Compressive Sensing Techniques (13 papers), Optimization and Variational Analysis (11 papers) and Advanced Optimization Algorithms Research (10 papers). Ying Cui collaborates with scholars based in United States, China and Singapore. Ying Cui's co-authors include Jong‐Shi Pang, Defeng Sun, Kim-Chuan Toh, Bodhisattva Sen, Xudong Li, Chenlei Leng, Xinyuan Zhao, Chao Ding, Ziyu He and Tsung‐Hui Chang and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Mathematical Programming and SIAM Journal on Optimization.

In The Last Decade

Ying Cui

24 papers receiving 284 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ying Cui United States	11	143	114	112	50	48	27	297
Pham Dinh Tao France	5	73 0.5×	97 0.9×	119 1.1×	35 0.7×	48 1.0×	6	312
Li-Ping Pang China	10	130 0.9×	237 2.1×	211 1.9×	65 1.3×	26 0.5×	85	399
Damek Davis United States	8	202 1.4×	197 1.7×	176 1.6×	18 0.4×	8 0.2×	32	406
Alexander Gasnikov Russia	11	208 1.5×	87 0.8×	128 1.1×	49 1.0×	37 0.8×	98	400
Charles Dossal France	9	114 0.8×	60 0.5×	41 0.4×	9 0.2×	10 0.2×	18	234
Aleksandr Moiseevich Rubinov Australia	5	73 0.5×	214 1.9×	193 1.7×	21 0.4×	28 0.6×	6	363
Jianchao Bai China	9	179 1.3×	130 1.1×	121 1.1×	11 0.2×	11 0.2×	36	310
Motakuri V. Ramana United States	6	85 0.6×	338 3.0×	268 2.4×	31 0.6×	12 0.3×	7	467
Paul Tseng United States	8	124 0.9×	125 1.1×	127 1.1×	14 0.3×	8 0.2×	8	309
Max Simchowitz United States	6	148 1.0×	39 0.3×	52 0.5×	19 0.4×	17 0.4×	18	326

Countries citing papers authored by Ying Cui

Since Specialization

Citations

This map shows the geographic impact of Ying Cui's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ying Cui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ying Cui more than expected).

Fields of papers citing papers by Ying Cui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ying Cui. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ying Cui. The network helps show where Ying Cui may publish in the future.

Co-authorship network of co-authors of Ying Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Ying Cui. A scholar is included among the top collaborators of Ying Cui based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ying Cui. Ying Cui is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Cui, Ying, et al.. (2024). Analysis of a Class of Minimization Problems Lacking Lower Semicontinuity. Mathematics of Operations Research. 50(3). 2175–2198. 3 indexed citations

He, Ziyu, et al.. (2023). Comparing solution paths of sparse quadratic minimization with a Stieltjes matrix. Mathematical Programming. 204(1-2). 517–566. 3 indexed citations

Sun, Ju, et al.. (2023). Implications of Solution Patterns on Adversarial Robustness. 32. 2393–2400.

Cui, Ying, et al.. (2023). Optimization for Robustness Evaluation Beyond ℓ_p Metrics. 97. 1–5. 1 indexed citations

Wu, Can, Ying Cui, Donghui Li, & Defeng Sun. (2023). Convex and Nonconvex Risk-Based Linear Regression at Scale. INFORMS journal on computing. 35(4). 797–816. 1 indexed citations

Cui, Ying, et al.. (2021). On Degenerate Doubly Nonnegative Projection Problems. Mathematics of Operations Research. 47(3). 2219–2239.

Cui, Ying, et al.. (2021). Asymptotic Properties of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization. Mathematics of Operations Research. 47(3). 2034–2064. 4 indexed citations

Cui, Ying, Chao Ding, Xudong Li, & Xinyuan Zhao. (2021). Augmented Lagrangian Methods for Convex Matrix Optimization Problems. Journal of the Operations Research Society of China. 10(2). 305–342. 6 indexed citations

Cui, Ying & Jong‐Shi Pang. (2021). Modern Nonconvex Nondifferentiable Optimization. Society for Industrial and Applied Mathematics eBooks. 31 indexed citations

10.

Cui, Ying, Ziyu He, & Jong‐Shi Pang. (2020). Nonconvex robust programming via value-function optimization. Computational Optimization and Applications. 78(2). 411–450. 1 indexed citations

11.

Cui, Ying, et al.. (2020). Two-Stage Stochastic Programming with Linearly Bi-parameterized Quadratic Recourse. SIAM Journal on Optimization. 30(3). 2530–2558. 10 indexed citations

12.

Cui, Ying, Ziyu He, & Jong‐Shi Pang. (2020). MultiComposite Nonconvex Optimization for Training Deep Neural Networks. SIAM Journal on Optimization. 30(2). 1693–1723. 14 indexed citations

13.

Cui, Ying, Defeng Sun, & Kim-Chuan Toh. (2019). Computing the Best Approximation over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone. SIAM Journal on Optimization. 29(4). 2785–2813. 3 indexed citations

14.

Cui, Ying, et al.. (2019). Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes. SIAM Journal on Optimization. 29(3). 2337–2362. 10 indexed citations

15.

Wang, Shuai, Tsung‐Hui Chang, Ying Cui, & Jong‐Shi Pang. (2019). Clustering by Orthogonal Non-negative Matrix Factorization: A Sequential Non-convex Penalty Approach. 5576–5580. 13 indexed citations

16.

Cui, Ying, Defeng Sun, & Kim-Chuan Toh. (2018). On the R-superlinear convergence of the KKT residuals generated by the augmented Lagrangian method for convex composite conic programming. Mathematical Programming. 178(1-2). 381–415. 25 indexed citations

17.

Davoine, Franck, et al.. (2018). A Fast and Accurate Matrix Completion Method Based on QR Decomposition and <inline-formula> <tex-math notation="LaTeX">$L_{2,1}$ </tex-math> </inline-formula>-Norm Minimization. IEEE Transactions on Neural Networks and Learning Systems. 30(3). 803–817. 32 indexed citations

18.

Cui, Ying, Jong‐Shi Pang, & Bodhisattva Sen. (2018). Composite Difference-Max Programs for Modern Statistical Estimation Problems. SIAM Journal on Optimization. 28(4). 3344–3374. 32 indexed citations

19.

Cui, Ying, Chao Ding, & Xinyuan Zhao. (2017). Quadratic Growth Conditions for Convex Matrix Optimization Problems Associated with Spectral Functions. SIAM Journal on Optimization. 27(4). 2332–2355. 16 indexed citations

20.

Cui, Ying, Xudong Li, Defeng Sun, & Kim-Chuan Toh. (2016). On the Convergence Properties of a Majorized Alternating Direction Method of Multipliers for Linearly Constrained Convex Optimization Problems with Coupled Objective Functions. Journal of Optimization Theory and Applications. 169(3). 1013–1041. 33 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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