Package: graDiEnt
Title: Stochastic Quasi-Gradient Differential Evolution Optimization
Version: 1.1.0
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/issues
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: stats, parallel
Suggests: testthat (>= 3.0.0)
RoxygenNote: 7.3.2
Repository: https://bmgaldo.r-universe.dev
Date/Publication: 2026-10-18 19:16:38 UTC
RemoteUrl: https://github.com/bmgaldo/gradient
RemoteRef: HEAD
RemoteSha: 132ec615c2ff3dd46bc73a8db004496c3c28bd2e
NeedsCompilation: no
Packaged: 2026-10-18 21:02:21 UTC; root
Author: Brendan Matthew Galdo [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-1279-3859>)
Maintainer: Brendan Matthew Galdo <Brendan.m.galdo@gmail.com>