# optax_lion { #phasic.optax_wrapper.optax_lion }

```python
phasic.optax_wrapper.optax_lion(
    learning_rate=0.0001,
    b1=0.9,
    b2=0.99,
    weight_decay=0.0,
)
```

Create Optax Lion optimizer wrapped for phasic.

Lion (Evolved Sign Momentum) is a memory-efficient optimizer discovered
through program search. It uses sign-based updates and typically requires
a lower learning rate than Adam.

## Parameters {.doc-section .doc-section-parameters}

<code>[**learning_rate**]{.parameter-name} [:]{.parameter-annotation-sep} [[float](`float`) or [optax](`optax`).[Schedule](`optax.Schedule`)]{.parameter-annotation} [ = ]{.parameter-default-sep} [1e-4]{.parameter-default}</code>

:   Learning rate. Typically 3-10x smaller than Adam.

<code>[**b1**]{.parameter-name} [:]{.parameter-annotation-sep} [[float](`float`)]{.parameter-annotation} [ = ]{.parameter-default-sep} [0.9]{.parameter-default}</code>

:   Exponential decay rate for first moment.

<code>[**b2**]{.parameter-name} [:]{.parameter-annotation-sep} [[float](`float`)]{.parameter-annotation} [ = ]{.parameter-default-sep} [0.99]{.parameter-default}</code>

:   Exponential decay rate for the gradient moving average.

<code>[**weight_decay**]{.parameter-name} [:]{.parameter-annotation-sep} [[float](`float`)]{.parameter-annotation} [ = ]{.parameter-default-sep} [0.0]{.parameter-default}</code>

:   Weight decay coefficient.

## Returns {.doc-section .doc-section-returns}

<code>[]{.parameter-name} [:]{.parameter-annotation-sep} [[OptaxOptimizer](`phasic.optax_wrapper.OptaxOptimizer`)]{.parameter-annotation}</code>

:   Wrapped Lion optimizer compatible with phasic SVGD.

## References {.doc-section .doc-section-references}

Chen et al. (2023). Symbolic Discovery of Optimization Algorithms.
arXiv:2302.06675. https://arxiv.org/abs/2302.06675