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APPROX
Experimental Design for Any p-Norm

Authors: Lap Chi Lau, Robert Wang, and Hong Zhou

Published in: LIPIcs, Volume 275, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)


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.

Cite as

Lap Chi Lau, Robert Wang, and Hong Zhou. Experimental Design for Any p-Norm. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 4:1-4:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@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-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023.4},
  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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