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
192
Using Randomization to Break the Curse of Dimensionality
John Rust · Econometrica · 1997 · 412 citations
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238
Massachusetts Institute of Technology
Cambridge, United States
10 h-index
16 publications
736 citations
University of Michigan
Ann Arbor, United States
7 h-index
10 publications
384 citations
University of California, Los Angeles
Los Angeles, United States
5 h-index
5 publications
264 citations
Stanford University
Stanford, United States
5 h-index
6 publications
141 citations

University of Padua
Padua, Italy
4 h-index
6 publications
703 citations
California Institute of Technology
Pasadena, United States
4 h-index
8 publications
826 citations
University of Maryland, College Park
College Park, United States
4 h-index
7 publications
470 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
4 h-index
7 publications
318 citations
1–8 of 238
13 publications · 698 citations
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8 publications · 966 citations
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7 publications · 353 citations
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7 publications · 188 citations
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Repository · 7 publications · 251 citations
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