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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
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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 |
Reference manual: | graDiEnt.pdf |
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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These binaries (installable software) and packages are in development.
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