Derivative-free optimization for parameter estimation in computational nuclear physics. (5th February 2015)

• Record Type: Journal Article
• Authors: Wild, Stefan M; Sarich, Jason; Schunck, Nicolas
• Is Part Of: Journal of physics. Volume 42:Number 3(2015:Mar.)
• Publication Date: 2015-02-05
• Subjects: numerical optimization -- parameter estimation -- density functional theory -- sensitivity analysis; Nuclear physics -- Periodicals; Particles (Nuclear physics) -- Periodicals; Physique nucléaire -- Périodiques; Particules (Physique nucléaire) -- Périodiques; Kernfysica; Elementaire deeltjes; 539.7
• DOI: 10.1088/0954-3899/42/3/034031
• Languages: English
• ISSNs: 0954-3899

Abstract

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 thePOUNDERS 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 thePOUNDERS software in the Toolkit for advanced optimization.