SID-PSM: A pattern search method guided by simplex derivatives for use in derivative-free optimization (version 1.3)

SID-PSM (version 1.3) is a suite of MATLAB [1] functions for numerically solving constrained or unconstrained nonlinear optimization problems, using derivative-free methods. In the general constrained case and for the current version, the derivatives of the functions de ning the constraints must be provided. The optimizer uses an implementation of a generalized pattern search method, combining its global convergence properties with the eciency of the use of quadratic polynomials to enhance the search step and of the use of simplex gradients for guiding the function evaluations of the poll step. An advantage of using SID-PSM, when compared to other pattern search implementations, is the reduction achieved in the total number of function evaluations required. In this document, we will mention the target problems suited for optimization with this code, describe the SID-PSM algorithm, and provide implementation details (including information about the MATLAB functions coded as well as guidelines to obtain, install, and customize the package). The software is freely available for research, educational or commercial use, under a GNU lesser general public license, but we expect that all publications describing work using this software quote the two following references. (1) A. L. Custodio and L. N. Vicente, Using sampling and simplex derivatives in pattern search methods, SIAM Journal on Optimization, 18 (2007) 537{555 (ref. [10]); (2) A. L. Custodio, H. Rocha, and L. N. Vicente, Incorporating minimum Frobenius norm models in direct search, Computational Optimization and Applications 46 (2010), 265{278 (ref. [9]).