World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
10937
World Ranking
5834
National Ranking
774

Mathematics

D-Index
49
Citations
10965
World Ranking
1137
National Ranking
62

Overview

Xiaoming Yuan is affiliated with the University of Hong Kong in China and has developed a research profile spanning several interconnected fields including engineering, computer science, and mathematics. Their work bridges theory and application, focusing especially on computational mechanics, numerical analysis, and electrical and electronic engineering.

Their publication record reflects significant involvement in areas such as sparse and compressive sensing techniques, advanced optimization algorithms research, and stochastic gradient optimization techniques. Other notable topics in their research include advanced numerical methods in computational mathematics, optimization and variational analysis, model reduction and neural networks, as well as numerical methods in inverse problems.

Xiaoming Yuan has contributed papers to a variety of scientific venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • Inverse Problems
  • SSRN Electronic Journal
  • Mathematical Programming
  • Set-Valued and Variational Analysis

Among their recent papers, notable examples are:

  • "An ADMM numerical approach to linear parabolic state constrained optimal control problems," 2020, Numerische Mathematik
  • "A General Descent Aggregation Framework for Gradient-Based Bi-Level Optimization," 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "A globally convergent proximal Newton-type method in nonsmooth convex optimization," 2022, Mathematical Programming
  • "Phase-transition-free rivets for layered oxide potassium cathodes," 2024, Nano Research
  • "A Generalized Primal-Dual Algorithm with Improved Convergence Condition for Saddle Point Problems," 2022, SIAM Journal on Imaging Sciences

Their collaborative network is notable, with frequent coauthors including Shangzhi Zeng, Jin Zhang, Yongcun Song, Hangrui Yue, and Bingsheng He, indicating ongoing collaborations across various research projects and topics.

Best Publications

  • On the $O(1/n)$ Convergence Rate of the Douglas-Rachford Alternating Direction Method

    Bingsheng He;Xiaoming Yuan

  • The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

    Caihua Chen;Bingsheng He;Yinyu Ye;Xiaoming Yuan

  • Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations

    Min Tao;Xiaoming Yuan

  • Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization

    Junfeng Yang;Xiaoming Yuan

  • Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming

    Bingsheng He;Min Tao;Xiaoming Yuan

  • Convergence Analysis of Primal-Dual Algorithms for a Saddle-Point Problem: From Contraction Perspective

    Bingsheng He;Xiaoming Yuan

  • Sparse and low-rank matrix decomposition via alternating direction method

    Xiaoming Yuan;Junfeng Yang

  • On non-ergodic convergence rate of Douglas---Rachford alternating direction method of multipliers

    Bingsheng He;Xiaoming Yuan

  • Solving Constrained Total-variation Image Restoration and Reconstruction Problems via Alternating Direction Methods

    Michael K. Ng;Pierre Weiss;Xiaoming Yuan

  • A Note on the Alternating Direction Method of Multipliers

    Deren Han;Xiaoming Yuan

  • Matrix completion via an alternating direction method

    Caihua Chen;Bingsheng He;Xiaoming Yuan

  • Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers

    Raymond H. Chan;Min Tao;Xiaoming Yuan

  • A STRICTLY CONTRACTIVE PEACEMAN-RACHFORD SPLITTING METHOD FOR CONVEX PROGRAMMING.

    Bingsheng He;Han Liu;Zhaoran Wang;Xiaoming Yuan

  • An approximate proximal-extragradient type method for monotone variational inequalities

    Bing Sheng He;Zhen Hua Yang;Xiao Ming Yuan

  • Adaptive Primal-Dual Hybrid Gradient Methods for Saddle-Point Problems

    Tom Goldstein;Min Li;Xiaoming Yuan;Ernie Esser

  • A splitting method for separable convex programming

    Bingsheng He;Min Tao;Xiaoming Yuan

  • On the Convergence of Primal-Dual Hybrid Gradient Algorithm

    Bingsheng He;Yanfei You;Xiaoming Yuan

  • The Linearized Alternating Direction Method of Multipliers for Dantzig Selector

    Unknown

  • On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable Convex Programming

    Bingsheng He;Bingsheng He;Liusheng Hou;Xiaoming Yuan

  • Local Linear Convergence of the Alternating Direction Method of Multipliers for Quadratic Programs

    Deren Han;Xiaoming Yuan

  • A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton

    Risheng Liu;Pan Mu;Xiaoming Yuan;Shangzhi Zeng

  • Linearized Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming

    Bingsheng He;Xiaoming Yuan

Frequent Co-Authors

Bingsheng He
Bingsheng He Nanjing University
Deren Han
Deren Han Nanjing Normal University
Jane J. Ye
Jane J. Ye University of Victoria
Michael K. Ng
Michael K. Ng Hong Kong Baptist University
Risheng Liu
Risheng Liu Dalian University of Technology
Tom Goldstein
Tom Goldstein University of Maryland, College Park
Raymond H. Chan
Raymond H. Chan Lingnan University
Mário A. T. Figueiredo
Mário A. T. Figueiredo Instituto Superior Técnico
Hai Yang
Hai Yang Hong Kong University of Science and Technology
Heinz H. Bauschke
Heinz H. Bauschke University of British Columbia

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