Exact optimal experimental designs with constraints

Mercedes Esteban-Bravo, Agata Leszkiewicz, Jose M. Vidal-Sanz*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)


The experimental design literature has produced a wide range of algorithms optimizing estimator variance for linear models where the design-space is finite or a convex polytope. But these methods have problems handling nonlinear constraints or constraints over multiple treatments. This paper presents Newton-type algorithms to compute exact optimal designs in models with continuous and/or discrete regressors, where the set of feasible treatments is defined by nonlinear constraints. We carry out numerical comparisons with other state-of-art methods to show the performance of this approach.

Original languageEnglish
Pages (from-to)845-863
Number of pages19
JournalStatistics and computing
Issue number3
Publication statusPublished - 1 May 2017
Externally publishedYes


  • constrained designs
  • Exact optimal experimental designs
  • Newton-type algorithms


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