Abstract
Finding a c-optimal design of a regression model is a basic optimization problem in statistics. We study the computational complexity of the problem in the case of a finite experimental domain. We formulate a decision version of the problem and prove its \(\boldsymbol{\mathit{NP}}\)-completeness. We provide examples of computationally complex instances of the design problem, motivated by cryptography. The problem, being \(\boldsymbol{\mathit{NP}}\)-complete, is then relaxed; we prove that a decision version of the relaxation, called approximate c-optimality, is P-complete. We derive an equivalence theorem for linear programming: we show that the relaxed c-optimality is equivalent (in the sense of many-one LOGSPACE-reducibility) to general linear programming.
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Černý, M., Hladík, M. Two complexity results on c-optimality in experimental design. Comput Optim Appl 51, 1397–1408 (2012). https://doi.org/10.1007/s10589-010-9377-8
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DOI: https://doi.org/10.1007/s10589-010-9377-8