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Experimental Design for Any p-Norm

Authors Lap Chi Lau, Robert Wang, Hong Zhou

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  • 21 pages

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Author Details

Lap Chi Lau
  • David R. Cheriton School of Computer Science, University of Waterloo, Canada
Robert Wang
  • David R. Cheriton School of Computer Science, University of Waterloo, Canada
Hong Zhou
  • School of Mathematics and Statistics, Fuzhou University, China


We thank Mohit Singh for bringing the Φ_p objective function to our attention. We also thank anonymous reviewers of an earlier version of this manuscript for helpful suggestions.

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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)


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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
  • Theory of computation → Rounding techniques
  • Approximation Algorithm
  • Optimal Experimental Design
  • Randomized Local Search


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