Two complexity results on \(c\)-optimality in experimental design

DOI10.1007/s10589-010-9377-8zbMath1245.90052OpenAlexW2025447593MaRDI QIDQ429521

Milan Hladík, Michal Černý

Publication date: 19 June 2012

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-010-9377-8

zbMATH Keywords

NP-completenessoptimal designP-completeness

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Abstract computational complexity for mathematical programming problems (90C60) Linear programming (90C05)

Related Items (4)

An unexpected connection between Bayes \(A\)-optimal designs and the group Lasso ⋮ Near-optimal discrete optimization for experimental design: a regret minimization approach ⋮ Design of c-optimal experiments for high-dimensional linear models ⋮ Gaussian Process Landmarking on Manifolds

Cites Work

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• Computing \(c\)-optimal experimental designs using the simplex method of linear programming
• Linear programming is log-space hard for P
• PRIMES is in P

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