Installation
About
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.
github.com/bmgaldo/graDiEnt | |
Bug report | File report |
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Maintainer
Maintainer | Brendan Matthew Galdo |