SciPy-like differential evolution for JAX
Fully jitted optimization of any JAX-compatible function. Serial and parallel execution on CPU, GPU, and TPU.
Installation
pip install mutax
Quick start
import jax.numpy as jnp
from mutax import differential_evolution
def cost_function(xs):
return jnp.sum(xs**2)
bounds = [(-5, 5)] * 10 # 10-dimensional problem with bounds for each dimension
result = differential_evolution(cost_function, bounds)
print("Best solution:", result.x)
print("Objective value:", result.fun)
Documentation
The documentation is available at Read the Docs.
1 maintainer
Metadata
1 maintainer
Release files for mutax 0.5.4
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| mutax-0.5.4.tar.gz | 6.3 kB | Details |
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| mutax-0.5.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 13.1 kB
Release files / mutax-0.5.4.tar.gz
| Download URL | mutax-0.5.4.tar.gz |
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| Size | 6.3 kB |
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Release files / mutax-0.5.4-py3-none-any.whl
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| Size | 6.8 kB |
| Tags | Python 3 |
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uv/0.12.10 {"installer":{"name":"uv","version":"0.12.10","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}
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