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.