Large-scale Semidefinite Programming

Large-Scale Semidefinite Programming is a specialized form of convex optimization that involves the minimization of a linear objective function over the intersection of the cone of positive semidefinite matrices with an affine subspace, specifically characterized by high-dimensional matrix variables and constraints that render traditional solution methods computationally infeasible.

71

Publications

8.7K

Citations

119

Authors

74

Institutions

Publications per year

2017–2026

26

Variants

Large-scale Sdp

Authors

119

  1. KT
    Kim-Chuan Toh

    National University of Singapore, Singapore

    7 publications · 314 citations

    7

    h-index

  2. DS
    Defeng Sun

    National University of Singapore, Singapore

    5 publications · 537 citations

    5

    h-index

  3. MY
    Makoto Yamashita

    NTT Basic Research Laboratories, Japan

    5 publications · 199 citations

    5

    h-index

  4. KF
    Katsuki Fujisawa

    Tokyo Institute of Technology, Japan

    5 publications · 143 citations

    5

    h-index

  5. MF
    Mituhiro Fukuda

    Tokyo Institute of Technology, Japan

    5 publications · 508 citations

    5

    h-index

1–5 of 119

Venues

  1. SIAM Journal on Optimization

    9 publications · 882 citations

    9

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 103 citations

    5

    h-index

  3. Optimization methods & software

    5 publications · 173 citations

    5

    h-index

  4. Mathematical Programming

    5 publications · 371 citations

    5

    h-index

  5. IEEE Transactions on Automatic Control

    4 publications · 424 citations

    4

    h-index

1–5