Finite-horizon Approximation

Finite-Horizon Approximation is a methodological framework in discounted reward optimization that approximates infinite-horizon problems by truncating the decision horizon to a finite time span, thereby enabling computationally tractable solutions while preserving asymptotic optimality under diminishing discount factors.

192

Publications

9.5K

Citations

426

Authors

238

Institutions

Publications per year

2017–2026

47

Authors

426

  1. RL
    Robert L. Smith

    University of Michigan, United States

    3 publications · 183 citations

    3

    h-index

  2. NH
    Nir Halman

    Hebrew University of Jerusalem, Israel

    3 publications · 138 citations

    3

    h-index

  3. MD
    Min Dai

    National University of Singapore, Singapore

    3 publications · 258 citations

    3

    h-index

  4. DS
    David Simchi‐Levi

    Massachusetts Institute of Technology, United States

    3 publications · 138 citations

    3

    h-index

  5. ZZ
    Zhengyuan Zhou

    Stanford University, United States

    3 publications · 38 citations

    3

    h-index

1–5 of 426

Venues

  1. Operations Research

    13 publications · 698 citations

    13

    h-index

  2. IEEE Transactions on Automatic Control

    8 publications · 966 citations

    8

    h-index

  3. Mathematics of Operations Research

    7 publications · 353 citations

    7

    h-index

  4. SIAM Journal on Control and Optimization

    7 publications · 188 citations

    7

    h-index

  5. arXiv (Cornell University)

    Repository · 7 publications · 251 citations

    7

    h-index

1–5