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.
