Pyepo

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:tada: We're happy to announce the PyEPO 0.3.8 release. :tada:

We're thrilled to bring you some exciting new features in this release:

We add a data generator `pyepo.data.portfolio.genData` for portfolio optimization and the corresponding Gurobi model `pyepo.model.grb.portfolioModel`. See details in our docs for [data](https://khalil-research.github.io/PyEPO/build/html/content/examples/data.html#portfolio) and [optimization model](https://khalil-research.github.io/PyEPO/build/html/content/examples/model.html#portfolio).

This synthetic dataset comes from [Smart “Predict, then Optimize”](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2020.3922) with detailed implementation guidelines provided in Appendix-D of the [supplemental material](https://pubsonline.informs.org/doi/suppl/10.1287/mnsc.2020.3922/suppl_file/mnsc.2020.3922.sm2.pdf).

Additionally, we have addressed several minor bugs to ensure a smoother user experience.

We're eager for you to test these out and share your feedback with us. As always, thank you for being a part of our growing community!

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:tada: We're happy to announce the PyEPO 0.3.7 release. :tada:

We're thrilled to bring you some exciting new features in this release:

We add an autograd module `pyepo.func.adaptiveImplicitMLE`, which uses the perturb-and-MAP framework and adaptively chooses the interpolation step size. This module samples noise perturbation from a Sum-of-Gamma distribution, subsequently interpolating the loss function for a more precise finite difference approximation. There is the corresponding paper [Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models](https://ojs.aaai.org/index.php/AAAI/article/view/26103). See details in our [docs](https://khalil-research.github.io/PyEPO/build/html/content/examples/function.html#adaptive-implicit-maximum-likelihood-estimator-ai-mle).

We're eager for you to test these out and share your feedback with us. As always, thank you for being a part of our growing community!

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:tada: We're happy to announce the PyEPO 0.3.6 release on PyPI. :tada:

The package is now available on [PyPI](https://pypi.org/project/pyepo/) for installation. You can easily install `PyEPO` using pip by running the following command:

bash
pip install pyepo

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:tada: We're happy to announce the PyEPO 0.3.5 release. :tada:

We're thrilled to bring you some exciting new features in this release:

- We add an autograd module `pyepo.func.implicitMLE`, which uses the perturb-and-MAP framework. This module samples noise perturbation from a Sum-of-Gamma distribution, subsequently interpolating the loss function for a more precise finite difference approximation. There is the corresponding paper [Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions](https://proceedings.neurips.cc/paper_files/paper/2021/hash/7a430339c10c642c4b2251756fd1b484-Abstract.html). See details in our [docs](https://khalil-research.github.io/PyEPO/build/html/content/examples/function.html#implicit-maximum-likelihood-estimator-i-mle).
- PyEPO is now compatible with [COPT](https://shanshu.ai/copt) (Cardinal Optimizer) API, one of the fastest solvers for various optimization problems.

We're eager for you to test these out and share your feedback with us. As always, thank you for being a part of our growing community!

<p align="center"><img width="30%" src="images/logo1.png" /></p>

We're happy to announce the 0.3.3 release.

We fix the sign bug of `pyepo.func.NCE`, and add modules `pyepo.func.contrastiveMAP` and `pyepo.func.negativeIdentity`. See details in our [docs](https://khalil-research.github.io/PyEPO/build/html/content/examples/function.html#noise-contrastive-estimation-nce).

There are the corresponding papers [Contrastive losses and solution caching for predict-and-optimize](https://www.ijcai.org/proceedings/2021/390) and [Backpropagation through combinatorial algorithms: Identity with projection works](https://arxiv.org/abs/2205.15213).

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We're happy to announce the 0.3.0 release.

Thank ijskar to add new end-to-end predict-then-optimize methods: noise contrastive estimation and learning to rank. We add modules `pyepo.func.NCE`, `pyepo.func.pointwiseLTR`, `pyepo.func.pairwiseLTR` and `pyepo.func.listwiseLTR`. See details in our [docs](https://khalil-research.github.io/PyEPO/build/html/content/examples/function.html#noise-contrastive-estimation-nce).

There are the corresponding papers [Contrastive losses and solution caching for predict-and-optimize](https://www.ijcai.org/proceedings/2021/390) and [Decision-focused learning: through the lens of learning to rank](https://proceedings.mlr.press/v162/mandi22a.html).