Value-at-risk Optimization

Value-At-Risk Optimization is a stochastic optimization framework that belongs to the class of risk-averse decision-making models, specifically distinguished by its objective to minimize the maximum loss exposure at a given confidence level under probabilistic uncertainty.

190

Publications

23.9K

Citations

407

Authors

252

Institutions

Publications per year

2017–2026

62

Variants

Var Optimization

Authors

407

  1. SU
    Stan Uryasev

    University of Florida, United States

    10 publications · 11.8K citations

    10

    h-index

  2. RT
    R. T. Rockafellar

    University of Washington, United States

    4 publications · 10.8K citations

    4

    h-index

  3. MX
    Minghui Xu

    Wuhan University, China

    4 publications · 401 citations

    4

    h-index

  4. SM
    Shie Mannor

    Technion – Israel Institute of Technology, Israel

    4 publications · 308 citations

    4

    h-index

  5. AT
    Aviv Tamar

    Technion – Israel Institute of Technology, Israel

    4 publications · 308 citations

    4

    h-index

1–5 of 407

Venues

  1. SSRN Electronic Journal

    Repository · 14 publications · 1.1K citations

    12

    h-index

  2. European Journal of Operational Research

    9 publications · 532 citations

    9

    h-index

  3. arXiv (Cornell University)

    Repository · 9 publications · 354 citations

    9

    h-index

  4. Operations Research

    7 publications · 1.1K citations

    7

    h-index

  5. SIAM Journal on Optimization

    7 publications · 386 citations

    7

    h-index

1–5