Python interface to multiple optimization solvers

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  • License: Mozilla Public License 2.0 (MPL 2.0)
  • Author: Yue Yang
  • Requires: Python >=3.8
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Project description

PyOptInterface (Python Optimization Interface)

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.

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. 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:

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.

In order to provide out-of-the-box support for open source optimizers (currently we support 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, 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:

Short Example

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:

@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 as well:

@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, fmtlib and martinus/unordered_dense as dependencies.

The design of PyOptInterface is inspired by JuMP.jl.

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.

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The following attestation bundles were made for pyoptinterface-0.3.0-cp38-cp38-macosx_10_14_x86_64.whl:

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