Registry / ai-ml / better-optimize

better-optimize

library0.4.2pypypi✓ verified 85d ago

A drop-in replacement for scipy.optimize functions with quality-of-life improvements, such as automatic gradient computation via JAX, progress bars, and early stopping. Current version is 0.4.1, released in 2025. The project is under active development with monthly releases.

ai-ml data

pip install better-optimize

github ↗package ↗

INSTALL

IMPORT

SIG · BETTER-OPTIMIZE

better-optimize

ai-mlpythonv0.4.2

Install

11.5s avg

Import

3329ms

Disk

305MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.3.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 2.756s · 320.6MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 11.5s · import 2.570s · 308MB

305MB installed

● package 305MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

minimize

✓ from better_optimize import minimize

✗ from scipy.optimize import minimize

better-optimize provides a drop-in replacement; using scipy's version won't get the QoL improvements.

Minimize the Rosenbrock function using BFGS. Note: JAX arrays are used for automatic differentiation.

import jax.numpy as jnp from better_optimize import minimize def rosen(x): return (1 - x[0])**2 + 100 * (x[1] - x[0]**2)**2 x0 = jnp.array([0.0, 0.0]) res = minimize(rosen, x0, method='BFGS') print(res.x)

Debug

Known issues

breakingThis library requires Python >=3.12. Attempting to install on older versions will fail.

fix

Upgrade Python to 3.12 or later.

affects: 0.4.1

gotchaThe library expects JAX arrays as inputs when using JAX-based gradients. Using NumPy arrays may cause errors or unexpected behavior.

fix

Convert NumPy arrays to JAX arrays via jnp.array(your_array).

affects: 0.3.0+

deprecatedThe 'callback' parameter in earlier versions was renamed to 'progress_callback'. The old name still works but will be removed in 0.5.0.

fix

Use 'progress_callback' instead of 'callback'.

affects: 0.3.0 - 0.4.1

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'better_optimize'

Library not installed or installed under a different name.

fix

Run 'pip install better-optimize' and ensure you are using the correct import name 'better_optimize' (underscore).

TypeError: expected JAX array, got numpy.ndarray

Passing NumPy arrays directly to functions that expect JAX arrays.

fix

Convert inputs to JAX arrays: x_jax = jnp.array(x_numpy).

Upgrade

Version history

0.4.2latest on PyPI · released May 23, 2026

Audit

Dependencies

scipyrequiredRequired for compatibility with scipy.optimize functions; better-optimize wraps them.

jaxoptionalEnables automatic gradient computation (optional, but recommended for most features).

Agent activity

21 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/jessegrabowski/better_optimize ↗

packagehttps://pypi.org/project/better-optimize/ ↗