Convex Relaxations

Convex Relaxations is a mathematical optimization technique that reformulates a non-convex optimization problem into a convex problem by enlarging the feasible set to include non-integer or non-extreme points, thereby providing a tractable lower bound for global optimization while preserving the structure necessary for efficient solution methods.

2.4K

Publications

173.3K

Citations

4.1K

Authors

1.2K

Institutions

Publications per year

2017–2026

611

Authors

4.1K

  1. SH
    Steven H. Low

    California Institute of Technology, United States

    21 publications · 3.7K citations

    19

    h-index

  2. DC
    Daniel Cremers

    Technical University of Munich, Germany

    20 publications · 1.5K citations

    18

    h-index

  3. JB
    Jean B. Lasserre

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

    20 publications · 2K citations

    17

    h-index

  4. MK
    Masakazu Kojima

    Tokyo Institute of Technology, Japan

    18 publications · 1.3K citations

    15

    h-index

  5. JL
    Javad Lavaei

    University of California, Berkeley, United States

    17 publications · 1K citations

    14

    h-index

1–5 of 4.1K

Venues

  1. Mathematical Programming

    149 publications · 14.1K citations

    58

    h-index

  2. SIAM Journal on Optimization

    89 publications · 14.5K citations

    47

    h-index

  3. IEEE Transactions on Power Systems

    65 publications · 8K citations

    45

    h-index

  4. Journal of Optimization Theory and Applications

    93 publications · 3.8K citations

    34

    h-index

  5. IEEE Transactions on Signal Processing

    56 publications · 5.3K citations

    34

    h-index

1–5