| Name | spinopt JSON |
| Version |
24.7.2
JSON |
| download |
| home_page | None |
| Summary | Scipy interface to NLOPT |
| upload_time | 2025-07-24 21:37:15 |
| maintainer | None |
| docs_url | None |
| author | Martin van der Schans |
| requires_python | None |
| license | None |
| keywords |
optimization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# SpiNopt: a Scipy optimization interface to NLOPT
For optimization, everyone starts out with the [Scipy optimization library](https://docs.scipy.org/doc/scipy/tutorial/optimize.html), but, at some point, you might want to try something else.
[NLOPT](https://nlopt.readthedocs.io/en/latest/) is a great library, but can be quite a hassle rewrite your code to use it.
This package provides a Scipy interface to the NLOPT optimization library. It's aim is not to provide a complete ecosystem which different solvers are available, but merely a way to quickly connect the NLOPT solvers, once you already have something set up with Scipy, or are familiar with how to set something up with Scipy.
## Basic example
```python
import numpy as np
from spinopt import NLOptimizer
dim = 3
# Define objective in this way
def my_easy_func(x, grad):
if grad.size > 0:
grad[:] = 2 * (x - np.arange(len(x)))
x = x - np.arange(len(x))
return x.dot(x)
# Define constraints Scipy style
A = np.ones((1, dim))
b = np.ones((1, 1))
constraints = [{"type": "eq", "jac": lambda w: A, "fun": lambda w: A.dot(w) - b.squeeze()}]
# Initialize optimizer
x0=np.zeros(dim)
opt = NLOptimizer(my_easy_func, x0, constraints=constraints)
# Optimize
res = opt.minimize()
assert res.success
assert np.allclose(res.x, np.arange(dim), atol=1e-5)
```
## Installation
To install from PyPI:
```bash
pip install spinopt
```
To install the latest development version from github:
```bash
pip install git+https://github.com/mvds314/spinopt.git
```
## Development
For development purposes, clone the repo:
```bash
git clone https://github.com/mvds314/spinopt.git
```
Then navigate to the folder containing `setup.py` and run
```bash
pip install -e .
```
to install the package in edit mode.
Run unittests with `pytest`.
## Related software
- [Scipy optimize](https://docs.scipy.org/doc/scipy/tutorial/optimize.html)
- [NLOPT](https://nlopt.readthedocs.io/en/latest/)
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"description": "# SpiNopt: a Scipy optimization interface to NLOPT\n\nFor optimization, everyone starts out with the [Scipy optimization library](https://docs.scipy.org/doc/scipy/tutorial/optimize.html), but, at some point, you might want to try something else.\n[NLOPT](https://nlopt.readthedocs.io/en/latest/) is a great library, but can be quite a hassle rewrite your code to use it.\n\nThis package provides a Scipy interface to the NLOPT optimization library. It's aim is not to provide a complete ecosystem which different solvers are available, but merely a way to quickly connect the NLOPT solvers, once you already have something set up with Scipy, or are familiar with how to set something up with Scipy.\n\n## Basic example\n\n```python\nimport numpy as np\nfrom spinopt import NLOptimizer\n\ndim = 3\n\n# Define objective in this way\ndef my_easy_func(x, grad):\n if grad.size > 0:\n grad[:] = 2 * (x - np.arange(len(x)))\n x = x - np.arange(len(x))\n return x.dot(x)\n\n# Define constraints Scipy style\nA = np.ones((1, dim))\nb = np.ones((1, 1))\nconstraints = [{\"type\": \"eq\", \"jac\": lambda w: A, \"fun\": lambda w: A.dot(w) - b.squeeze()}]\n\n# Initialize optimizer\nx0=np.zeros(dim)\nopt = NLOptimizer(my_easy_func, x0, constraints=constraints)\n\n# Optimize\nres = opt.minimize()\nassert res.success\nassert np.allclose(res.x, np.arange(dim), atol=1e-5)\n```\n\n## Installation\n\nTo install from PyPI:\n\n```bash\npip install spinopt\n```\n\nTo install the latest development version from github:\n\n```bash\npip install git+https://github.com/mvds314/spinopt.git\n```\n\n## Development\n\nFor development purposes, clone the repo:\n\n```bash\ngit clone https://github.com/mvds314/spinopt.git\n```\n\nThen navigate to the folder containing `setup.py` and run\n\n```bash\npip install -e .\n```\n\nto install the package in edit mode.\n\nRun unittests with `pytest`.\n\n## Related software\n\n- [Scipy optimize](https://docs.scipy.org/doc/scipy/tutorial/optimize.html)\n- [NLOPT](https://nlopt.readthedocs.io/en/latest/)\n\n",
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