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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.1 |
rolling source/ R- | graDiEnt_1.0.1.tar.gz |
9.6 KiB |
1.0.1 |
rolling linux/jammy R-4.5 | graDiEnt_1.0.1.tar.gz |
38.2 KiB |
1.0.1 |
rolling linux/noble R-4.5 | graDiEnt_1.0.1.tar.gz |
38.1 KiB |
1.0.1 |
latest source/ R- | graDiEnt_1.0.1.tar.gz |
9.6 KiB |
1.0.1 |
latest linux/jammy R-4.5 | graDiEnt_1.0.1.tar.gz |
38.2 KiB |
1.0.1 |
latest linux/noble R-4.5 | graDiEnt_1.0.1.tar.gz |
38.1 KiB |
1.0.1 |
2026-04-23 source/ R- | graDiEnt_1.0.1.tar.gz |
9.6 KiB |
1.0.1 |
2026-04-09 windows/windows R-4.5 | graDiEnt_1.0.1.zip |
41.0 KiB |
1.0.1 |
2025-04-20 source/ R- | graDiEnt_1.0.1.tar.gz |
9.6 KiB |