4 papers
Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
Raghu Bollapragada, Matt Menickelly, Witold Nazarewicz +3
We address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of par…
Tuning Multigrid Methods with Robust Optimization
Jed Brown, Yunhui He, Scott MacLachlan +2
Local Fourier analysis is a useful tool for predicting and analyzing the performance of many efficient algorithms for the solution of discretized PDEs, such as multigrid and domain…
Derivative-free optimization methods
Jeffrey Larson, Matt Menickelly, Stefan M. Wild
In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of…
Robust Learning of Trimmed Estimators via Manifold Sampling
Matt Menickelly, Stefan M. Wild
We adapt a manifold sampling algorithm for the nonsmooth, nonconvex formulations of learning that arise when imposing robustness to outliers present in the training data. We demons…