License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2023.4
URN: urn:nbn:de:0030-drops-188292
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18829/
Lau, Lap Chi ;
Wang, Robert ;
Zhou, Hong
Experimental Design for Any p-Norm
Abstract
We consider a general p-norm objective for experimental design problems that captures some well-studied objectives (D/A/E-design) as special cases. We prove that a randomized local search approach provides a unified algorithm to solve this problem for all nonnegative integer p. This provides the first approximation algorithm for the general p-norm objective, and a nice interpolation of the best known bounds of the special cases.
BibTeX - Entry
@InProceedings{lau_et_al:LIPIcs.APPROX/RANDOM.2023.4,
author = {Lau, Lap Chi and Wang, Robert and Zhou, Hong},
title = {{Experimental Design for Any p-Norm}},
booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)},
pages = {4:1--4:21},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-296-9},
ISSN = {1868-8969},
year = {2023},
volume = {275},
editor = {Megow, Nicole and Smith, Adam},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/18829},
URN = {urn:nbn:de:0030-drops-188292},
doi = {10.4230/LIPIcs.APPROX/RANDOM.2023.4},
annote = {Keywords: Approximation Algorithm, Optimal Experimental Design, Randomized Local Search}
}
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Keywords: |
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Approximation Algorithm, Optimal Experimental Design, Randomized Local Search |
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Collection: |
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Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023) |
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Issue Date: |
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2023 |
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Date of publication: |
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04.09.2023 |
