Approximate Optimization
Approximate Optimization is a computational strategy within the domain of bounded rationality that, as a method, prescribes the use of simplified models or heuristics to yield solutions that are sufficiently close to the optimal outcome when exact optimization is infeasible due to cognitive, temporal, or informational constraints.
736
Publications
47.3K
Citations
1.9K
Authors
721
Institutions
Publications per year
2017–2026
340
Parents
736
On the Convergence of Pattern Search Algorithms
Virginia Torczon · SIAM Journal on Optimization · 1997 · 1.3K citations
1–5 of 736
1.9K
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721
Argonne National Laboratory
Lemont, United States
18 h-index
40 publications
2K citations
Massachusetts Institute of Technology
Cambridge, United States
16 h-index
27 publications
5.8K citations
University of California, Berkeley
Berkeley, United States
15 h-index
24 publications
2.6K citations

Kanazawa University
Kanazawa, Japan
12 h-index
31 publications
1.4K citations
Google (United States)
Mountain View, United States
12 h-index
102 publications
38.2K citations

University of Notre Dame
Notre Dame, United States
12 h-index
29 publications
1.3K citations
University of California, Los Angeles
Los Angeles, United States
11 h-index
12 publications
669 citations
Yonsei University
Seoul, South Korea
11 h-index
20 publications
499 citations
1–8 of 721
Repository · 57 publications · 2.7K citations
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PRA · 24 publications · 1.3K citations
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19 publications · 1.8K citations
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15 publications · 1.2K citations
13
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12 publications · 537 citations
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