Penalized Optimization

Penalized Optimization is a method in plan optimization that incorporates penalty terms into the objective function to systematically discourage undesirable solution characteristics, thereby differentiating it from other optimization methods by explicitly balancing solution quality against constraint violations or complexity.

475

Publications

26.7K

Citations

933

Authors

492

Institutions

Publications per year

2017–2026

111

Authors

933

  1. XY
    Xiaoqi Yang

    Hong Kong Polytechnic University, Hong Kong SAR China

    16 publications · 631 citations

    13

    h-index

  2. SL
    Stefano Lucidi

    Sapienza University of Rome, Italy

    8 publications · 334 citations

    8

    h-index

  3. GD
    Gianni Di Pillo

    Sapienza University of Rome, Italy

    7 publications · 321 citations

    7

    h-index

  4. XH
    Xiaoxu Huang

    Technical University of Denmark, Denmark

    7 publications · 225 citations

    7

    h-index

  5. ZW
    Zhongping Wan

    Wuhan University, China

    7 publications · 203 citations

    7

    h-index

1–5 of 933

Venues

  1. Mathematical Programming

    29 publications · 2.6K citations

    23

    h-index

  2. Journal of Optimization Theory and Applications

    32 publications · 1.2K citations

    21

    h-index

  3. SIAM Journal on Optimization

    23 publications · 1.8K citations

    20

    h-index

  4. arXiv (Cornell University)

    Repository · 16 publications · 410 citations

    13

    h-index

  5. Journal of Global Optimization

    11 publications · 635 citations

    11

    h-index

1–5