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MIPLearn
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Bump version to 0.4

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dev v0.4
Alinson S. Xavier 2 years ago
parent 752885660d
commit 702824a3b5
2 changed files with 16 additions and 15 deletions
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2
Makefile
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@ -3,7 +3,7 @@ PYTEST := pytest
PIP := $(PYTHON) -m pip PIP := $(PYTHON) -m pip
MYPY := $(PYTHON) -m mypy MYPY := $(PYTHON) -m mypy
PYTEST_ARGS := -W ignore::DeprecationWarning -vv --log-level=DEBUG PYTEST_ARGS := -W ignore::DeprecationWarning -vv --log-level=DEBUG
VERSION := 0.3 VERSION := 0.4
all: docs test all: docs test

29
README.md
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@ -22,21 +22,22 @@ Documentation
------------- -------------
- Tutorials: - Tutorials:
1. [Getting started (Pyomo)](https://anl-ceeesa.github.io/MIPLearn/0.3/tutorials/getting-started-pyomo/) 1. [Getting started (Pyomo)](https://anl-ceeesa.github.io/MIPLearn/0.4/tutorials/getting-started-pyomo/)
2. [Getting started (Gurobipy)](https://anl-ceeesa.github.io/MIPLearn/0.3/tutorials/getting-started-gurobipy/) 2. [Getting started (Gurobipy)](https://anl-ceeesa.github.io/MIPLearn/0.4/tutorials/getting-started-gurobipy/)
3. [Getting started (JuMP)](https://anl-ceeesa.github.io/MIPLearn/0.3/tutorials/getting-started-jump/) 3. [Getting started (JuMP)](https://anl-ceeesa.github.io/MIPLearn/0.4/tutorials/getting-started-jump/)
4. [User cuts and lazy constraints](https://anl-ceeesa.github.io/MIPLearn/0.4/tutorials/cuts-gurobipy/)
- User Guide - User Guide
1. [Benchmark problems](https://anl-ceeesa.github.io/MIPLearn/0.3/guide/problems/) 1. [Benchmark problems](https://anl-ceeesa.github.io/MIPLearn/0.4/guide/problems/)
2. [Training data collectors](https://anl-ceeesa.github.io/MIPLearn/0.3/guide/collectors/) 2. [Training data collectors](https://anl-ceeesa.github.io/MIPLearn/0.4/guide/collectors/)
3. [Feature extractors](https://anl-ceeesa.github.io/MIPLearn/0.3/guide/features/) 3. [Feature extractors](https://anl-ceeesa.github.io/MIPLearn/0.4/guide/features/)
4. [Primal components](https://anl-ceeesa.github.io/MIPLearn/0.3/guide/primal/) 4. [Primal components](https://anl-ceeesa.github.io/MIPLearn/0.4/guide/primal/)
5. [Learning solver](https://anl-ceeesa.github.io/MIPLearn/0.3/guide/solvers/) 5. [Learning solver](https://anl-ceeesa.github.io/MIPLearn/0.4/guide/solvers/)
- Python API Reference - Python API Reference
1. [Benchmark problems](https://anl-ceeesa.github.io/MIPLearn/0.3/api/problems/) 1. [Benchmark problems](https://anl-ceeesa.github.io/MIPLearn/0.4/api/problems/)
2. [Collectors & extractors](https://anl-ceeesa.github.io/MIPLearn/0.3/api/collectors/) 2. [Collectors & extractors](https://anl-ceeesa.github.io/MIPLearn/0.4/api/collectors/)
3. [Components](https://anl-ceeesa.github.io/MIPLearn/0.3/api/components/) 3. [Components](https://anl-ceeesa.github.io/MIPLearn/0.4/api/components/)
4. [Solvers](https://anl-ceeesa.github.io/MIPLearn/0.3/api/solvers/) 4. [Solvers](https://anl-ceeesa.github.io/MIPLearn/0.4/api/solvers/)
5. [Helpers](https://anl-ceeesa.github.io/MIPLearn/0.3/api/helpers/) 5. [Helpers](https://anl-ceeesa.github.io/MIPLearn/0.4/api/helpers/)
Authors Authors
------- -------
@ -58,7 +59,7 @@ 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: 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, Xiaoyi Gu, Berkay Becu, Santanu S. Dey.** *MIPLearn: An Extensible Framework for Learning-Enhanced Optimization (Version 0.3)*. Zenodo (2023). DOI: [10.5281/zenodo.4287567](https://doi.org/10.5281/zenodo.4287567) * **Alinson S. Xavier, Feng Qiu, Xiaoyi Gu, Berkay Becu, Santanu S. Dey.** *MIPLearn: An Extensible Framework for Learning-Enhanced Optimization (Version 0.4)*. Zenodo (2024). 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: 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:

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