Randomized Optimization Algorithms

Randomized Optimization Algorithms is a class of optimization algorithms that belong to the broader category of optimization methods, distinguished by the deliberate incorporation of randomness in their search or update mechanisms to improve efficiency, escape local optima, or handle uncertainty in the objective function or constraints.

217

Publications

13.1K

Citations

490

Authors

247

Institutions

Publications per year

2017–2026

59

Authors

490

  1. RT
    Roberto Tempo

    Politecnico di Torino, Italy

    9 publications · 498 citations

    9

    h-index

  2. FD
    Fabrizio Dabbene

    Institute of Electronics, Computer and Telecommunication Engineering, Italy

    6 publications · 221 citations

    6

    h-index

  3. JL
    John Lygeros

    ETH Zurich, Switzerland

    5 publications · 175 citations

    5

    h-index

  4. MC
    Michael C. Fu

    University of Maryland, College Park, United States

    5 publications · 222 citations

    5

    h-index

  5. SI
    Steven I. Marcus

    University of Maryland, College Park, United States

    5 publications · 222 citations

    5

    h-index

1–5 of 490

Venues

  1. IEEE Transactions on Automatic Control

    8 publications · 378 citations

    8

    h-index

  2. SIAM Journal on Optimization

    7 publications · 1.1K citations

    7

    h-index

  3. Mathematical Programming

    6 publications · 253 citations

    6

    h-index

  4. Neural Information Processing Systems

    Conference · 5 publications · 131 citations

    5

    h-index

  5. arXiv (Cornell University)

    Repository · 5 publications · 109 citations

    5

    h-index

1–5