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graDiEnt
View on CRAN: Click
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Download and install graDiEnt package within the R console
Install from CRAN:
install.packages("graDiEnt")
Install from Github:
library("remotes")
install_github("cran/graDiEnt")
Install by package version:
library("remotes")
install_version("graDiEnt", "1.0.1")
Attach the package and use:
library("graDiEnt")
Maintained by
Brendan Matthew Galdo
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-10
Latest Update: 2022-05-10
Description:
An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; ). 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.
How to cite:
Brendan Matthew Galdo (2022). graDiEnt: Stochastic Quasi-Gradient Differential Evolution Optimization. R package version 1.0.1, https://cran.r-project.org/web/packages/graDiEnt
Previous versions and publish date:
No previous versions
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Other R packages that graDiEnt depends,
imports, suggests or enhances
Functions, R codes and Examples using
the graDiEnt R package
Some associated functions: GetAlgoParams . optim_SQGDE .
Some associated R codes: GetAlgoParams.R . Purify.R . SQG_DE_bin_1_best.R . SQG_DE_bin_1_curr.R . SQG_DE_bin_1_rand.R . optim_SQGDE.R . Full graDiEnt package functions and examples
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