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A Python library for heuristically solving mixed integer programs

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# HIPS
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Documentation](https://github.com/cxlvinchau/hips/actions/workflows/main.yml/badge.svg)](https://cxlvinchau.github.io/hips) [![Test](https://github.com/cxlvinchau/hips/actions/workflows/test.yml/badge.svg)](https://github.com/cxlvinchau/hips/actions/workflows/test.yml) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cxlvinchau/hips-examples/blob/main/notebooks/mip-example.ipynb)

HIPS is a Python library that implements various well-established heuristics for mixed-integer programming, e.g. the feasibility pump [[FGL05]](https://cxlvinchau.github.io/hips/references.html#id4), RENS [[Ber13]](https://cxlvinchau.github.io/hips/references.html#id6) and multiple diving heuristics. Since many heuristics rely on a linear programming solver, HIPS implements wrappers for powerful solvers like Gurobi [[GurobiOptimizationLLC21]](https://cxlvinchau.github.io/hips/references.html#id9) and Clp [[jVR+20]](https://cxlvinchau.github.io/hips/references.html#id8). Thus, a great strength of HIPS is its ability to switch solvers for different use cases.

Primarily, HIPS is targeted at an academic audience and facilitates the implementation of new heuristics. It is designed to be easily extensible and user-friendly. Further, it is straightforward to add new linear programming solvers by implementing the corresponding interface.

## Authors
Severin Bals (Technical University of Munich) and Calvin Chau (Technical University of Munich)

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