From 8c8ea8f9322ef6346311384eb7c4ea592c0f323c Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Tue, 18 May 2021 11:48:41 -0500 Subject: [PATCH] Update README.md --- README.md | 58 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) diff --git a/README.md b/README.md index 8d6a28b..8820ace 100644 --- a/README.md +++ b/README.md @@ -14,3 +14,61 @@ **MIPLearn** is an extensible open-source framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). See the [main repository](https://github.com/ANL-CEEESA/MIPLearn) for more information. This repository holds an experimental Julia interface for the package. [miplearn]: https://github.com/ANL-CEEESA/MIPLearn + +## 1. Usage + +### 1.1 Installation + +To use MIPLearn.jl, the first step is to [install the Julia programming language on your machine](https://julialang.org/). After Julia is installed, launch the Julia console, type `]` to switch to package manager mode, then run: + +``` +(@v1.6) pkg> add MIPLearn@0.2 +``` + +This command should also automatically install all the required Python dependencies. To test that the package has been correctly installed, run (in package manager mode): + +``` +(@v1.6) pkg> test MIPLearn +``` + +If you find any issues installing the package, please do not hesitate to [open an issue](https://github.com/ANL-CEEESA/MIPLearn.jl/issues). + + +### 1.2 Describing instances + + +### 1.3 Solving instances and training + +### 1.4 Saving and loading solver state + +### 1.5 Solving training instances in parallel + +## 2. Customization + +### 2.1 Selecting solver components + +### 2.2 Adjusting component aggresiveness + +### 2.3 Evaluating component performance + +### 2.4 Using customized ML classifiers and regressors + +## 3. 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. +* Based upon work supported by the **U.S. Department of Energy Advanced Grid Modeling Program** under Grant DE-OE0000875. + +## 4. Citing MIPLearn + +If you use MIPLearn in your research (either the solver 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](https://doi.org/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](https://doi.org/10.1287/ijoc.2020.0976) + +## 5. License + + +Released under the modified BSD license. See `LICENSE` for more details. +