BOOSTERS: A DERIVATIVE-FREE ALGORITHM BASED ON RADIAL BASIS FUNCTIONS

R. Oeuvray∗, M. Bierlaire∗∗
2009 International Journal of Modelling and Simulation  
Derivative-free optimization involves the methods used to minimize an expensive objective function when its derivatives are not available. We present here a trust-region algorithm based on Radial Basis Functions (RBFs). The main originality of our approach is the use of RBFs to build the trust-region models and our management of the interpolation points based on Newton fundamental polynomials. Moreover the complexity of our method is very attractive. We have tested the algorithm against the
more » ... state-of-theart methods (UOBYQA, NEWUOA, DFO). The tests on the problems from the CUTEr collection show that BOOSTERS is performing very well on medium-size problems. Moreover, it is able to solve problems of dimension 200, which is considered very large in derivative-free optimization.
doi:10.2316/journal.205.2009.1.205-4634 fatcat:neci7asnkrcevi4dai7vbi3bve