Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Michael Braun
Title: trustOptim: An R Package for Trust Region Optimization with Sparse Hessians
Abstract: Trust region algorithms are nonlinear optimization tools that tend to be stable and reliable when the objective function is non-concave, ill-conditioned, or exhibits regions that are nearly flat. Additionally, most freely-available optimization routines do not exploit the sparsity of the Hessian when such sparsity exists, as in log posterior densities of Bayesian hierarchical models. The trustOptim package for the R programming language addresses both of these issues. It is intended to be robust, scalable and efficient for a large class of nonlinear optimization problems that are often encountered in statistics, such as finding posterior modes. The user must supply the objective function, gradient and Hessian. However, when used in conjunction with the sparseHessianFD package, the user does not need to supply the exact sparse Hessian, as long as the sparsity structure is known in advance. For models with a large number of parameters, but for which most of the cross-partial derivatives are zero (i.e., the Hessian is sparse), trustOptim offers dramatic performance improvements over existing options, in terms of computational time and memory footprint.

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Paper: trustOptim: An R Package for Trust Region Optimization with Sparse Hessians     Download PDF (Downloads: 2753)
trustOptim_0.8.4.2.tar.gz: R source package Download (Downloads: 325; 416KB) R code for replication of examples Download (Downloads: 274; 5KB)

DOI: 10.18637/jss.v060.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.