DESCRIPTION

Package: graDiEnt
Title: Stochastic Quasi-Gradient Differential Evolution Optimization
Version: 1.0.1
Authors@R: 
    person(given = "Brendan Matthew",
           family = "Galdo",
           role = c("aut", "cre"),
           email = "Brendan.m.galdo@gmail.com",
           comment = c(ORCID = "0000-0002-1279-3859"))
Description: 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.
License: MIT + file LICENSE
URL: https://github.com/bmgaldo/graDiEnt
BugReports: https://github.com/bmgaldo/graDiEnt
Encoding: UTF-8
Depends:
    R (>= 3.5.0)
Imports: 
    stats,
    doParallel
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.2