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.
- Version1.0.1
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Last release05/10/2022
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Team
Brendan Matthew Galdo
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- Imports1 package