IWOPY¶
Added in version 1.0.0: One global iwopy optimizer factory iwopy.create_optimizer, for easy access of the large list of available choices from the Optimizer Catalog.
Fraunhofer IWES optimization tools in Python.
Define an optimization problem once and choose an iwopy or external solver through a shared Python interface. External integrations include:
iwopy’s GG and SLSQP optimizers are included. LocalFD supplies finite
differences; DiscretizeRegGrid wraps a problem on a regular grid. Population
evaluation batches candidates or derivative points when the backend supports it.
Choose a solver in the Optimizer Catalog: every selectable optimizer has a short description and an explicit, runnable Python setup. SciPy differential evolution, Nevergrad, Optuna, and DEAP support batched search.
For Users¶
Start with Installation, choose a solver in Optimizer Catalog, and explore Examples. Optimizer callbacks covers intermediate results; API provides the complete reference.
For Developers¶
Use Testing for quality checks and the repository’s development guide for test ownership and contribution requirements. Submit issues or pull requests on GitHub. iwopy is released under the MIT license.