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https://github.com/metab0t/pyoptinterface
Efficient modeling interface for mathematical optimization in Python
https://github.com/metab0t/pyoptinterface
conic-programs linear-programming mathematical-modelling mathematical-programming mixed-integer-programming modeling-language optimization python quadratic-programming
Last synced: 15 days ago
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Efficient modeling interface for mathematical optimization in Python
- Host: GitHub
- URL: https://github.com/metab0t/pyoptinterface
- Owner: metab0t
- License: other
- Created: 2023-06-13T16:11:33.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-04-22T09:48:57.000Z (9 months ago)
- Last Synced: 2024-04-29T10:38:41.145Z (8 months ago)
- Topics: conic-programs, linear-programming, mathematical-modelling, mathematical-programming, mixed-integer-programming, modeling-language, optimization, python, quadratic-programming
- Language: C++
- Homepage: https://metab0t.github.io/PyOptInterface/
- Size: 1.96 MB
- Stars: 82
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
PyOptInterface (Python Optimization Interface)
=======
[](https://pypi.org/pypi/pyoptinterface/)
**PyOptInterface** is an open-source Python library to provide a unified API to construct and solve optimization models with various optimizers.
The detailed documentation can be found [here](https://metab0t.github.io/PyOptInterface/).
## Key features compared with other modeling interfaces
It is designed as a very thin wrapper of native C API of optimizers and attempts to provide common abstractions of an algebraic modelling environment including model, variable, constraint and expression with the least overhead of performance.
The key features of PyOptInterface include:
- Very fast speed to construct optimization model (10x faster than Pyomo, comparable with JuMP.jl and some official Python bindings provided by vendors of optimizer)
- Highly efficient structured automatic differentiation for nonlinear optimization with JIT compilation (faster than other NLP frameworks)
- Low overhead to modify and re-solve the problem incrementally (including adding/removing variables/constraints, changing objective function, etc.)
- Unified API to cover common usages, write once and the code works for all optimizers
- You still have escape hatch to query or modify solver-specific parameter/attribute/information for different optimizers directly like the vendor-specific Python binding of optimizer
## Benchmark
The benchmark comparing PyOptInterface with some other modeling interfaces can be found [here](https://metab0t.github.io/PyOptInterface/benchmark.html). PyOptInterface is among the fastest modeling interfaces in terms of model construction time and automatic differentiation of nonlinear optimization problems.
## Installation
PyOptInterface is available on PyPI. You can install it via pip:
```
pip install pyoptinterface
```
After installation, you can import the package in Python console:
```python
import pyoptinterface as poi
```
PyOptInterface has no dependencies other than Python itself. However, to use it with a specific optimizer, you need to install the corresponding optimizer manually. The details can be found on [the configurations of optimizers](https://metab0t.github.io/PyOptInterface/getting_started.html).
In order to provide out-of-the-box support for open source optimizers (currently we support [HiGHS](https://github.com/ERGO-Code/HiGHS)), PyOptInterface can also be installed with pre-built optimizers. You can install them via pip:
```
pip install pyoptinterface[highs]
```
It will install a full-featured binary version of HiGHS optimizer via [highsbox](http://github.com/metab0t/highsbox), which can be used with PyOptInterface.
## What kind of problems can PyOptInterface solve?
It currently supports the following problem types:
- Linear Programming (LP)
- Mixed-Integer Linear Programming (MILP)
- Quadratic Programming (QP)
- Mixed-Integer Quadratic Programming (MIQP)
- Quadratically Constrained Quadratic Programming (QCQP)
- Mixed-Integer Quadratically Constrained Quadratic Programming (MIQCQP)
- Second-Order Cone Programming (SOCP)
- Mixed-Integer Second-Order Cone Programming (MISOCP)
- Exponential Cone Programming (ECP)
- Mixed-Integer Exponential Cone Programming (MIECP)
- Nonlinear Programming (NLP)
## What optimizers does PyOptInterface support?
It currently supports the following optimizers:
- [COPT](https://shanshu.ai/copt) ( Commercial )
- [Gurobi](https://www.gurobi.com/) ( Commercial )
- [HiGHS](https://github.com/ERGO-Code/HiGHS) ( Open source )
- [Mosek](https://www.mosek.com/) ( Commercial )
- [Ipopt](https://github.com/coin-or/Ipopt) ( Open source )
## Short Example
```python
import pyoptinterface as poi
from pyoptinterface import highs
model = highs.Model()
x = model.add_variable(lb=0, ub=1, domain=poi.VariableDomain.Continuous, name="x")
y = model.add_variable(lb=0, ub=1, domain=poi.VariableDomain.Integer, name="y")
con = model.add_linear_constraint(x+y, poi.Geq, 1.2, name="con")
obj = 2*x
model.set_objective(obj, poi.ObjectiveSense.Minimize)
model.set_model_attribute(poi.ModelAttribute.Silent, False)
model.optimize()
print(model.get_model_attribute(poi.ModelAttribute.TerminationStatus))
# TerminationStatusCode.OPTIMAL
x_val = model.get_value(x)
# 0.2
y_val = model.get_value(y)
# 1.0
```
## Citation
If you use PyOptInterface in your research, please consider citing [the following paper](https://arxiv.org/abs/2405.10130):
```bibtex
@misc{yang2024pyoptinterface,
title={PyOptInterface: Design and implementation of an efficient modeling language for mathematical optimization},
author={Yue Yang and Chenhui Lin and Luo Xu and Wenchuan Wu},
year={2024},
eprint={2405.10130},
archivePrefix={arXiv},
primaryClass={cs.MS}
}
```
If you use the nonlinear optimization feature of PyOptInterface, please consider citing [the following paper](https://ieeexplore.ieee.org/document/10721402) as well:
```bibtex
@article{yang2024accelerating,
title={Accelerating Optimal Power Flow with Structure-aware Automatic Differentiation and Code Generation},
author={Yang, Yue and Lin, Chenhui and Xu, Luo and Yang, Xiaodong and Wu, Wenchuan and Wang, Bin},
journal={IEEE Transactions on Power Systems},
year={2024},
publisher={IEEE}
}
```
## License
PyOptInterface is licensed under MPL-2.0 License.
It uses [nanobind](https://github.com/wjakob/nanobind), [fmtlib](https://github.com/fmtlib/fmt) and [martinus/unordered_dense](https://github.com/martinus/unordered_dense) as dependencies.
The design of PyOptInterface is inspired by [JuMP.jl](https://jump.dev).
Some solver-related code in `src` folder is adapted from the corresponding solver interface package in `JuMP.jl`
ecosystem, which is licensed under MIT License.
The header files in `thirdparty/solvers` directory are from the corresponding distribution of optimizers and are licensed under their own licenses.