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Experimental Design for Any p-Norm

Authors Lap Chi Lau, Robert Wang, Hong Zhou

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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

Funding

  • Lau, Lap Chi: Supported in part by NSERC Discovery Grant.
  • Wang, Robert: Supported in part by NSERC Discovery Grant and Canada Graduate Scholarship.
  • Zhou, Hong: Supported in part by Fuzhou University startup funding (XRC-22056) and Natural Science Foundation of Fujian Province of China (2023J01414).

Acknowledgements

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.

Cite AsGet BibTex

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)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2023.4

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.

Subject Classification

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

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References

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