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  "Description": "An optim-style implementation of the Stochastic\nQuasi-Gradient Differential Evolution (SQG-DE) optimization\nalgorithm first published by Sala, Baldanzini, and Pierini\n(2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization\nalgorithm fuses the robustness of the population-based global\noptimization algorithm \"Differential Evolution\" with the\nefficiency of gradient-based optimization. The derivative-free\nalgorithm uses population members to build stochastic gradient\nestimates, without any additional objective function\nevaluations. Sala, Baldanzini, and Pierini argue this algorithm\nis useful for 'difficult optimization problems under a tight\nfunction evaluation budget.' This package can run SQG-DE in\nparallel and sequentially.",
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  "URL": "https://github.com/bmgaldo/graDiEnt",
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