Implemented optimizers

The following implementations of optimizers can found under this extension package:

Pygmo implementations

These derivative-free global optimizers are ported from the pygmo Python library:

In order to use this optimizers you need to install the pygmo dependency:

pip install pygmo

Note

The pygmo library is not compatible with Python 3.9 yet and only available for Linux and Unix systems.

These gradient based optimizers are ported from the tensorflow keras Python library:

In order to use this optimizers you need to install the tensorflow dependency:

pip install tensorflow

These derivative-free global optimizers are ported from the nevergrad Python library:

In order to use this optimizers you need to install the nevergrad dependency:

pip install nevergrad

Name	Keyword argument	Reference
Differential Evolution	`"NevergradDE"`	nevergrad.optimizers.DifferentialEvolution
PSO	`"PSO"`	nevergrad.optimizers.ConfPSO

These derivative-free global optimizers are ported from the evosax Python library:

In order to use this optimizers you need to install the evosax dependency:

pip install evosax

Note

The evosax library is only available for Linux and Unix systems.

Name	Keyword argument	Reference
CMAES	`"EvoSaxCMAES"`	evosax.strategies.cma_es
PSO	`"EvoSaxPSO"`	evosax.strategies.pso
Simulated Annealing	`"EvoSaxSimAnneal"`	evosax.strategies.sim_anneal
Differential Evolution	`"EvoSaxDE"`	evosax.strategies.de

These derivative-free global optimizers are ported from the optuna Python library:

In order to use this optimizers you need to install the optuna dependency:

pip install optuna

Name	Keyword argument	Reference
Tree-structured Parzen Estimator	`"TPESampler"`	optuna.samplers.TPESampler

These derivative-free global optimizers are ported from the optax Python library:

In order to use this optimizers you need to install the optax dependency:

pip install optax

Name	Keyword argument	Reference
Adam	`"Adam"`	optax.adam
SGD	`"SGDOptax"`	optax.sgd