World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
84173
World Ranking
4667
National Ranking
2164

Overview

Lieven Vandenberghe is affiliated with the University of California, Los Angeles in the United States. Their research primarily spans across the fields of Computer Science, Mathematics, and Engineering. Within these domains, Vandenberghe's work extensively covers subfields including Computational Theory and Mathematics, Numerical Analysis, Computational Mechanics, Accounting, and Strategy and Management.

The scientist's main topics of research include Advanced Optimization Algorithms Research, Optimization and Variational Analysis, Sparse and Compressive Sensing Techniques, Matrix Theory and Algorithms, Risk and Portfolio Optimization, Taxation and Legal Issues, and Corporate Governance and Law.

Vandenberghe has contributed to several peer-reviewed publications. Among recent papers are:

  • "Bregman primal-dual first-order method and application to sparse semidefinite programming," 2021, Computational Optimization and Applications
  • "Disjoint Bilinear Optimization: A Two-Stage Robust Optimization Perspective," 2022, INFORMS Journal on Computing
  • "Bregman Three-Operator Splitting Methods," 2022, Journal of Optimization Theory and Applications
  • "Regularized Diffusion Adaptation via Conjugate Smoothing," 2021, IEEE Transactions on Automatic Control
  • "Linear optimization over homogeneous matrix cones," 2023, Acta Numerica

Frequent coauthors collaborating with Vandenberghe include Xin Jiang, Levent Tunçel, Jianzhe Zhen, Ahmadreza Marandi, and Danique de Moor.

The scientist publishes regularly in venues such as arXiv (Cornell University), INFORMS Journal on Computing, Journal of Optimization Theory and Applications, Computational Optimization and Applications, and IEEE Transactions on Automatic Control.

Best Publications

  • Convex Optimization

    Stephen Boyd;Lieven Vandenberghe

  • Semidefinite programming

    Lieven Vandenberghe;Stephen Boyd

  • Applications of second-order cone programming

    Miguel Sousa Lobo;Lieven Vandenberghe;Stephen Boyd;Hervé Lebret

  • A tutorial on geometric programming

    Stephen Boyd;Seung-Jean Kim;Lieven Vandenberghe;Arash Hassibi

  • Handbook of semidefinite programming : theory, algorithms, and applications

    Henry Wolkowicz;Romesh Saigal;Lieven Vandenberghe

  • Handbook of Semidefinite Programming

    Henry Wolkowicz;Romesh Saigal;Lieven Vandenberghe

  • Determinant Maximization with Linear Matrix Inequality Constraints

    Lieven Vandenberghe;Stephen Boyd;Shao-Po Wu

  • On- and off-line identification of linear state-space models

    Marc Moonen;Bart De Moor;Lieven Vandenberghe;Joos Vandewalle

  • Interior-Point Method for Nuclear Norm Approximation with Application to System Identification

    Zhang Liu;Lieven Vandenberghe

  • A primal-dual potential reduction method for problems involving matrix inequalities

    Lieven Vandenberghe;Stephen Boyd

  • Extended LMI characterizations for stability and performance of linear systems

    Goele Pipeleers;Bram Demeulenaere;Jan Swevers;Lieven Vandenberghe

  • FIR filter design via semidefinite programming and spectral factorization

    Shao-Po Wu;S. Boyd;L. Vandenberghe

  • Semidefinite programming duality and linear time-invariant systems

    V. Balakrishnan;L. Vandenberghe

  • Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

    Stephen Boyd;Lieven Vandenberghe

  • The generalized linear complementarity problem applied to the complete analysis of resistive piecewise-linear circuits

    L. Vandenberghe;B.L. de Moor;J. Vandewalle

  • FIR Filter Design via Spectral Factorization and Convex Optimization

    Shao-Po Wu;Stephen Boyd;Lieven Vandenberghe

  • Chordal Graphs and Semidefinite Optimization

    Lieven Vandenberghe;Martin S. Andersen

  • Nuclear norm system identification with missing inputs and outputs

    Zhang Liu;Anders Hansson;Lieven Vandenberghe

  • Covariance selection for nonchordal graphs via chordal embedding

    Joachim Dahl;Lieven Vandenberghe;Vwani Roychowdhury

  • Interior-point methods for large-scale cone programming

    Martin Skovgaard Andersen;Joachim Dahl;Zhang Liu;Lieven Vandenberghe

  • Convex Optimization: Convex optimization problems

    Stephen Boyd;Lieven Vandenberghe

Frequent Co-Authors

Stephen Boyd
Stephen Boyd Stanford University
Venkataramanan Balakrishnan
Venkataramanan Balakrishnan Purdue University West Lafayette
Marc Moonen
Marc Moonen KU Leuven
Chih-Kong Ken Yang
Chih-Kong Ken Yang University of California, Los Angeles
Bart De Moor
Bart De Moor KU Leuven
Abbas El Gamal
Abbas El Gamal Stanford University
Richard D. Wesel
Richard D. Wesel University of California, Los Angeles
Vwani P. Roychowdhury
Vwani P. Roychowdhury University of California, Los Angeles

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