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
Author:	Brendan Matthew Galdo [aut, cre]
Maintainer:	Brendan Matthew Galdo <Brendan.m.galdo at gmail.com>
BugReports:	https://github.com/bmgaldo/graDiEnt
License:	MIT + file LICENSE
URL:	https://github.com/bmgaldo/graDiEnt
NeedsCompilation:	no
Materials:	README NEWS
In views:	Optimization
CRAN checks:	graDiEnt results

Documentation:

Reference manual:

graDiEnt.pdf

Downloads:

Package source:	graDiEnt_1.0.1.tar.gz
Windows binaries:	r-devel: graDiEnt_1.0.1.zip, r-release: graDiEnt_1.0.1.zip, r-oldrel: graDiEnt_1.0.1.zip
macOS binaries:	r-release (arm64): graDiEnt_1.0.1.tgz, r-oldrel (arm64): graDiEnt_1.0.1.tgz, r-release (x86_64): graDiEnt_1.0.1.tgz, r-oldrel (x86_64): graDiEnt_1.0.1.tgz

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