On Multiple-Objective Nonlinear Optimal Designs

  • Qianshun ChengEmail author
  • Dibyen Majumdar
  • Min Yang
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


Experiments with multiple-objectives form a staple diet of modern scientific research. Deriving optimal designs with multiple-objectives is a long-standing challenging problem with few tools available. The few existing approaches cannot provide a satisfactory solution in general: either the computation is very expensive or a satisfactory solution is not guaranteed. There is need for a general approach which can effectively derive multi-objective optimal designs. A novel algorithm is proposed to address this literature gap. We prove convergence of this algorithm, and show in various examples that the new algorithm can derive the true solutions with high speed.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mathematics, Statistics, and Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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