Implementation techniques for the SCFO experimental optimization framework

The material presented in this document is intended as a comprehensive, implementation-oriented supplement to the experimental optimization framework presented in [Bunin, G.A., Francois, G., Bonvin, D.: Feasible-side global convergence in experimental optimization. SIAM J. Optim. (submitted) (2014)]. The issues of physical degradation, unknown Lipschitz constants, measurement/estimation noise, gradient estimation, sufficient excitation, and the handling of soft constraints and/or a numerical cost function are all addressed, and a robust, implementable version of the sufficient conditions for feasible-side global convergence is proposed.


Year:
2014
Publisher:
arXiV, Cornell University Library, arXiv:1406.3997 [math.OC]
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 Record created 2014-06-17, last modified 2018-01-28

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