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Adaptive sequencing of primal, dual, and design steps in simulation based optimization

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  • Torsten Bosse
  • Lutz Lehmann
  • Andreas Griewank

Abstract

Many researchers have used Oneshot optimization methods based on user-specified primal state iterations, the corresponding adjoint iterations, and appropriately preconditioned design steps. Our goal here is to develop heuristics for sequencing these three subtasks, in order to optimize the convergence rate of the resulting coupled iteration cycle. A key ingredient is the preconditioning in the design step by a BFGS approximation of the projected Hessian. We provide a hard bound on the spectral radius of the coupled iteration cycle at local minima satisfying second order sufficiency conditions. Finally, we show how certain problem specific parameters can be estimated by local samples and be used to steer the whole process adaptively. We present limited numerical results that confirm the theoretical analysis. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Torsten Bosse & Lutz Lehmann & Andreas Griewank, 2014. "Adaptive sequencing of primal, dual, and design steps in simulation based optimization," Computational Optimization and Applications, Springer, vol. 57(3), pages 731-760, April.
    • Handle: RePEc:spr:coopap:v:57:y:2014:i:3:p:731-760
    DOI: 10.1007/s10589-013-9606-z
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    References listed on IDEAS

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    1. Adel Hamdi & Andreas Griewank, 2011. "Reduced quasi-Newton method for simultaneous design and optimization," Computational Optimization and Applications, Springer, vol. 49(3), pages 521-548, July.
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