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.">

graDiEnt: Stochastic Quasi-Gradient Differential Evolution Optimization (original) (raw)

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

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