Package: graDiEnt 1.1.0

graDiEnt: Stochastic Quasi-Gradient Differential Evolution Optimization

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.

Authors:Brendan Matthew Galdo [aut, cre]

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Bug tracker:https://github.com/bmgaldo/gradient/issues

On CRAN:

2 exports 4 stars 1.26 score 4 dependencies 5 scripts 219 downloads

Last updated 28 days agofrom:d853e5349b. Checks:OK: 7. Indexed: yes.

Exports:GetAlgoParamsoptim_SQGDE

Dependencies:codetoolsdoParallelforeachiterators