Nevergrad: An open source tool for derivative-free optimization

This post introduces and open-sources Nevergrad, a Python library for derivative-free optimization. It offers a wide range of algorithms (e.g., differential evolution, CMA-ES, particle swarm optimization) in a standard ask-and-tell framework, enabling AI researchers to easily test and compare different optimizers for ML problems. The library includes benchmarks for evaluating algorithm performance on various problem types (multimodal, noisy, discrete, ill-conditioned) and can be used for hyperparameter tuning in ML models and other scientific applications.