# {py:mod}`evox.algorithms.so.de_variants.ode`

```{py:module} evox.algorithms.so.de_variants.ode
```

```{autodoc2-docstring} evox.algorithms.so.de_variants.ode
:allowtitles:
```

## Module Contents

### Classes

````{list-table}
:class: autosummary longtable
:align: left

* - {py:obj}`ODE <evox.algorithms.so.de_variants.ode.ODE>`
  - ```{autodoc2-docstring} evox.algorithms.so.de_variants.ode.ODE
    :summary:
    ```
````

### API

`````{py:class} ODE(pop_size: int, lb: torch.Tensor, ub: torch.Tensor, base_vector: typing.Literal[best, rand] = 'rand', num_difference_vectors: int = 1, differential_weight: float | torch.Tensor = 0.5, cross_probability: float = 0.9, mean: torch.Tensor | None = None, stdev: torch.Tensor | None = None, device: torch.device | None = None)
:canonical: evox.algorithms.so.de_variants.ode.ODE

Bases: {py:obj}`evox.core.Algorithm`

```{autodoc2-docstring} evox.algorithms.so.de_variants.ode.ODE
```

```{rubric} Initialization
```

```{autodoc2-docstring} evox.algorithms.so.de_variants.ode.ODE.__init__
```

````{py:method} init_step()
:canonical: evox.algorithms.so.de_variants.ode.ODE.init_step

```{autodoc2-docstring} evox.algorithms.so.de_variants.ode.ODE.init_step
```

````

````{py:method} step()
:canonical: evox.algorithms.so.de_variants.ode.ODE.step

```{autodoc2-docstring} evox.algorithms.so.de_variants.ode.ODE.step
```

````

`````