Online Approximation Algorithms
Online Approximation Algorithms is an algorithmic method that belongs to the class of approximation algorithms, distinguished by its ability to compute solutions for optimization problems in an online setting where input is revealed incrementally and irrevocably, without knowledge of future requests, while providing performance guarantees relative to the optimal offline solution.
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698
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316
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2017–2026
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Logarithmic regret algorithms for online convex optimization
Elad Hazan, Amit Agarwal, Satyen Kale · Machine Learning · 2007 · 874 citations · Full text
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Carnegie Mellon University
Pittsburgh, United States
16 h-index
36 publications
2.3K citations
Technion – Israel Institute of Technology
Haifa, Israel
15 h-index
20 publications
1.5K citations
Massachusetts Institute of Technology
Cambridge, United States
10 h-index
16 publications
7.5K citations
Google (United States)
Mountain View, United States
10 h-index
22 publications
1.7K citations

Tel Aviv University
Tel Aviv, Israel
10 h-index
15 publications
699 citations
Stanford University
Stanford, United States
8 h-index
14 publications
916 citations
University of Maryland, College Park
College Park, United States
8 h-index
15 publications
320 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
8 h-index
14 publications
828 citations
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Repository · 19 publications · 661 citations
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10 publications · 351 citations
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10 publications · 537 citations
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Repository · 9 publications · 230 citations
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TALG · 8 publications · 303 citations
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