Semidefinite Relaxation

Semidefinite Relaxation is a convex optimization technique that approximates a hard combinatorial problem, particularly in integer programming and global optimization, by replacing non-convex quadratic constraints with positive semidefinite matrix constraints, thereby providing a tractable bound on the original problem.

1.5K

Publications

111.8K

Citations

2.7K

Authors

846

Institutions

Publications per year

2017–2026

548

Authors

2.7K

  1. WM
    Wing‐Kin Ma

    Chinese University of Hong Kong, Hong Kong SAR China

    29 publications · 6.1K citations

    24

    h-index

  2. JB
    Jean B. Lasserre

    Laboratoire d'Analyse et d'Architecture des Systèmes, France

    28 publications · 2.3K citations

    20

    h-index

  3. GW
    Gang Wang

    Ningbo University, China

    20 publications · 1.1K citations

    19

    h-index

  4. DW
    Derrick Wing Kwan Ng

    UNSW Sydney, Australia

    20 publications · 3.3K citations

    18

    h-index

  5. FR
    Franz Rendl

    Graz University of Technology, Austria

    17 publications · 2.3K citations

    17

    h-index

1–5 of 2.7K

Venues

  1. IEEE Transactions on Signal Processing

    93 publications · 12.4K citations

    57

    h-index

  2. Mathematical Programming

    84 publications · 9.6K citations

    46

    h-index

  3. SIAM Journal on Optimization

    71 publications · 6.8K citations

    41

    h-index

  4. IEEE Transactions on Wireless Communications

    58 publications · 4.9K citations

    37

    h-index

  5. IEEE Transactions on Vehicular Technology

    55 publications · 3.5K citations

    33

    h-index

1–5