Proportional Volume Sampling and Approximation Algorithms for A -Optimal Design

Aleksandar Nikolov (), Mohit Singh () and Uthaipon (Tao) Tantipongpipat ()
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Aleksandar Nikolov: Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada
Mohit Singh: H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Uthaipon (Tao) Tantipongpipat: H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

Mathematics of Operations Research, 2022, vol. 47, issue 2, 847-877

Abstract: We study optimal design problems in which the goal is to choose a set of linear measurements to obtain the most accurate estimate of an unknown vector. We study the A -optimal design variant where the objective is to minimize the average variance of the error in the maximum likelihood estimate of the vector being measured. We introduce the proportional volume sampling algorithm to obtain nearly optimal bounds in the asymptotic regime when the number k of measurements made is significantly larger than the dimension d and obtain the first approximation algorithms whose approximation factor does not degrade with the number of possible measurements when k is small. The algorithm also gives approximation guarantees for other optimal design objectives such as D -optimality and the generalized ratio objective, matching or improving the previously best-known results. We further show that bounds similar to ours cannot be obtained for E -optimal design and that A -optimal design is NP-hard to approximate within a fixed constant when k = d .

Keywords: Primary: 90C27; secondary: 62J05; 62K05; 62K15; 68W20; 68W25; optimal design; design of experiments; A-optimal design; D-optimal design; generalized ratio objective; linear regression; volume sampling; proportional volume sampling; approximation algorithms; NP-hardness (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:47:y:2022:i:2:p:847-877

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