# About

### Authors

- Alinson S. Xavier, Argonne National Laboratory <axavier@anl.gov>
- Feng Qiu, Argonne National Laboratory <fqiu@anl.gov>

### Acknowledgments

- Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357, and the U.S. Department of Energy Advanced Grid Modeling Program under Grant DE-OE0000875.

### References

If you use MIPLearn in your research, or the included problem generators, we kindly request that you cite the package as follows:

- Alinson S. Xavier, Feng Qiu. MIPLearn: An Extensible Framework for Learning-Enhanced Optimization. Zenodo (2020). DOI: 10.5281/zenodo.4287567

If you use MIPLearn in the field of power systems optimization, we kindly request that you cite the reference below, in which the main techniques implemented in MIPLearn were first developed:

- Alinson S. Xavier, Feng Qiu, Shabbir Ahmed. Learning to Solve Large-Scale Unit Commitment Problems. INFORMS Journal on Computing (2020). DOI: 10.1287/ijoc.2020.0976

### License

    MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
    Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.

    Redistribution and use in source and binary forms, with or without modification, are permitted
    provided that the following conditions are met:

    1. Redistributions of source code must retain the above copyright notice, this list of
       conditions and the following disclaimer.
    2. Redistributions in binary form must reproduce the above copyright notice, this list of
       conditions and the following disclaimer in the documentation and/or other materials provided
       with the distribution.
    3. Neither the name of the copyright holder nor the names of its contributors may be used to
       endorse or promote products derived from this software without specific prior written
       permission.

    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
    CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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