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Adaptive grid semidefinite programming for finding optimal designs


We find optimal designs for linear models using a novel algorithm that iteratively combines a semidefinite programming (SDP) approach with adaptive grid techniques. The proposed algorithm is also adapted to find locally optimal designs for nonlinear models. The search space is first discretized, and SDP is applied to find the optimal design based on the initial grid. The points in the next grid set are points that maximize the dispersion function of the SDP-generated optimal design using nonlinear programming. The procedure is repeated until a user-specified stopping rule is reached. The proposed algorithm is broadly applicable, and we demonstrate its flexibility using (i) models with one or more variables and (ii) differentiable design criteria, such as A-, D-optimality, and non-differentiable criterion like E-optimality, including the mathematically more challenging case when the minimum eigenvalue of the information matrix of the optimal design has geometric multiplicity larger than 1. Our algorithm is computationally efficient because it is based on mathematical programming tools and so optimality is assured at each stage; it also exploits the convexity of the problems whenever possible. Using several linear and nonlinear models with one or more factors, we show the proposed algorithm can efficiently find optimal designs.

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Dette’s work was partially supported by the Collaborative Research Center “Statistical modeling of nonlinear dynamic processes” (SFB 823, Teilprojekt C2) of the German Research Foundation (DFG). Dette and Wong were also partially supported by a Grant from the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R01GM107639. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Correspondence to Belmiro P. M. Duarte.

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Duarte, B.P.M., Wong, W.K. & Dette, H. Adaptive grid semidefinite programming for finding optimal designs. Stat Comput 28, 441–460 (2018). https://doi.org/10.1007/s11222-017-9741-y

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  • Adaptive grid
  • Continuous design
  • Model-based optimal design
  • Nonlinear programming
  • Semidefinite programming

Mathematics Subject Classification

  • 62K05
  • 90C47