VXQR: derivative-free unconstrained optimization based on QR factorizations
This paper presents basic features of a new family of algorithms for unconstrained derivative-free optimization, based on line searches along directions generated from QR factorizations of past direction matrices. Emphasis is on fast descent with a low number of function values, so that the algorithm can be used for fairly expensive functions. The theoretical total time overhead needed per function evaluation is of order O(n(2)), where n is the problem dimension, but the observed overhead is much smaller. Numerical results are given for a particular algorithm VXQR1 from this family, implemented in Matlab, and evaluated on the scalability test set of Herrera et al. (http://www.sci2s.ugr.es/eamhco/CFP.php, 2010) for problems in dimensions n is an element of {50, 100, 200, 500, 1,000}. Performance depends a lot on the graph {(t, f(x + th)) | t is an element of [0, 1]} of the function along line segments. The algorithm is typically very fast on smooth problems with not too rugged graphs, and on problems with a roughly separable structure. It typically performs poorly on problems where the graph along many directions is highly multimodal without pronounced overall slope (e. g., for smooth functions with superimposed oscillations of significant size), where the graphs along many directions are piecewise constant (e. g., for problems minimizing a maximum norm), or where the function overflows on the major part of the search region and no starting point with finite function value is known.
derivative-free optimization line search scalability black box optimization high-dimensional global optimization expensive objective function