Exact optimal experimental designs with constraints Articles uri icon

publication date

  • May 2017

start page

  • 845

end page

  • 863

issue

  • 3

volume

  • 27

international standard serial number (ISSN)

  • 0960-3174

electronic international standard serial number (EISSN)

  • 1573-1375

abstract

  • 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.

keywords

  • exact optimal experimental designs; constrained designs; newton-type algorithms; integer nonlinear programs; genetic algorithms; optimization; sequences