Movatterモバイル変換

graDiEnt: Stochastic Quasi-Gradient Differential Evolution Optimization

An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially.

Version:	1.0.1
Depends:	R (≥ 3.5.0)
Imports:	stats,doParallel
Published:	2022-05-10
DOI:	10.32614/CRAN.package.graDiEnt
Author:	Brendan Matthew Galdo [aut, cre]
Maintainer:	Brendan Matthew Galdo <Brendan.m.galdo at gmail.com>
BugReports:	https://github.com/bmgaldo/graDiEnt
License:	MIT + fileLICENSE
URL:	https://github.com/bmgaldo/graDiEnt
NeedsCompilation:	no
Materials:	README,NEWS
In views:	Optimization
CRAN checks:	graDiEnt results

Documentation:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=graDiEntto link to this page.