CVX based algorithms for constructing various optimal regression designs




Wong, Weng Kee
Zhou, Julie

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Canadian Journal of Statistics


CVX-based numerical algorithms are widely and freely available for solving convex optimization problems but their applications to solve optimal design problems are limited. Using the CVX programs in MATLAB, we demonstrate their utility and flexibility over traditional algorithms in statistics for finding different types of optimal approximate designs under a convex criterion for nonlinear models. They are generally fast and easy to implement for any model and any convex optimality criterion. We derive theoretical properties of the algorithms and use them to generate new A-, c-, D- and E-optimal designs for various nonlinear models, including multi-stage and multi-objective optimal designs. We report properties of the optimal designs and provide sample CVX program codes for some of our examples that users can amend to find tailored optimal designs for their problems. The Canadian Journal of Statistics 47: 374–391; 2019 © 2019 Statistical Society of Canada



convex optimization, dose-response model, equivalence theorem, multi-objective design, multi-stage design, semi-definite programming


Wong, W. K. & Zhou, J. (2019). CVX based algorithms for constructing various optimal regression designs. Canadian Journal of Statistics, 47(3), 374-391.