Parallel Stochastic Global Optimization Using Radial Basis Functions
INFORMS Journal on Computing
Self-Correcting Geometry in Model-Based Algorithms for Derivative-Free Unconstrained Optimization
SIAM Journal on Optimization
Constrained multifidelity optimization using model calibration
Structural and Multidisciplinary Optimization
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We present a new derivative-free algorithm, ORBIT, for unconstrained local optimization of computationally expensive functions. A trust-region framework using interpolating Radial Basis Function (RBF) models is employed. The RBF models considered often allow ORBIT to interpolate nonlinear functions using fewer function evaluations than the polynomial models considered by present techniques. Approximation guarantees are obtained by ensuring that a subset of the interpolation points is sufficiently poised for linear interpolation. The RBF property of conditional positive definiteness yields a natural method for adding additional points. We present numerical results on test problems to motivate the use of ORBIT when only a relatively small number of expensive function evaluations are available. Results on two very different application problems, calibration of a watershed model and optimization of a PDE-based bioremediation plan, are also encouraging and support ORBIT's effectiveness on blackbox functions for which no special mathematical structure is known or available.