| Name | alpaqa JSON |
| Version |
0.0.1
JSON |
| download |
| home_page | https://github.com/kul-optec/alpaqa |
| Summary | Augmented Lagrangian and PANOC solvers for nonconvex numerical optimization |
| upload_time | 2022-08-06 18:16:15 |
| maintainer | |
| docs_url | None |
| author | Pieter P |
| requires_python | >=3.7 |
| license | |
| keywords |
optimization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/kul-optec/alpaqa/actions)
[](https://kul-optec.github.io/alpaqa/Coverage/index.html)
[](https://github.com/kul-optec/alpaqa)
# alpaqa
`Alpaqa` is an efficient implementation of the Augmented Lagrangian method for general nonlinear programming problems,
which uses the first-order, matrix-free PANOC algorithm as an inner solver.
The numerical algorithms themselves are implemented in C++ for optimal performance,
and they are exposed as an easy-to-use Python package.
The solvers in this library solve minimization problems of the following form:
<div align="center">
</div>
The objective function _f_(x) and the constraints function _g_(x)
should have a Lipschitz-continuous gradient.
## Documentation
[**Sphinx documentation**](https://kul-optec.github.io/alpaqa/Sphinx/index.html)
[**Doxygen documentation**](https://kul-optec.github.io/alpaqa/Doxygen/index.html)
[**Python examples**](https://kul-optec.github.io/alpaqa/Sphinx/examples/examples_landing_page.html)
[**C++ examples**](https://kul-optec.github.io/alpaqa/Doxygen/examples.html)
## Installation
The project is available on [PyPI](https://pypi.org/project/alpaqa):
```sh
python3 -m pip install alpaqa
```
For more information, please see the full
[installation instructions](https://kul-optec.github.io/alpaqa/Sphinx/install/installation.html).
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