2026-08-14T23:19:57Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18829 2024-03-06T11:02:40Z ddc:004 open_access

Experimental Design for Any p-Norm Lau, Lap Chi Wang, Robert Zhou, Hong Approximation Algorithm Optimal Experimental Design Randomized Local Search 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Lap Chi Lau and Robert Wang and Hong Zhou 2023 Is Part Of LIPIcs, Volume 275, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.APPROX/RANDOM.2023.4 urn:nbn:de:0030-drops-188292 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023.4 eng https://creativecommons.org/licenses/by/4.0/legalcode