Derivative-free optimization for parameter estimation in computational nuclear physics
Abstract
We consider optimization problems that arise when estimating a set of unknown parameters from experimental data, particularly in the context of nuclear density functional theory. We examine the cost of not having derivatives of these functionals with respect to the parameters. We show that the POUNDERS code for local derivative-free optimization obtains consistent solutions on a variety of computationally expensive energy density functional calibration problems. We also provide a primer on the operation of the POUNDERS software in the Toolkit for Advanced Optimization.
Cite
@article{arxiv.1406.5464,
title = {Derivative-free optimization for parameter estimation in computational nuclear physics},
author = {Stefan M. Wild and Jason Sarich and Nicolas Schunck},
journal= {arXiv preprint arXiv:1406.5464},
year = {2015}
}
Comments
17 pages, 4 figures, 5 tables; Invited paper for the Journal of Physics G: Nuclear and Particle Physics focus section entitled "Enhancing the interaction between nuclear experiment and theory through information and statistics". In press