PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming
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| Format: | Preprint |
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2022
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| _version_ | 1866911602120327168 |
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| author | Tang, Bo Khalil, Elias B. |
| author_facet | Tang, Bo Khalil, Elias B. |
| contents | In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters may be a priori unknown but can be estimated from contextual information. A typical predict-then-optimize approach separates predictions and optimization into two distinct stages. Recently, end-to-end predict-then-optimize has emerged as an attractive alternative. This work introduces the PyEPO package, a PyTorch-based end-to-end predict-then-optimize library in Python. To the best of our knowledge, PyEPO (pronounced like \textit{pineapple} with a silent ``n") is the first such generic tool for linear and integer programming with predicted objective function coefficients. It includes various algorithms such as surrogate decision losses, black-box solvers, and perturbed methods. PyEPO offers a user-friendly interface for defining new optimization problems, applying state-of-the-art algorithms, and using custom neural network architectures. We conducted experiments comparing various methods on problems such as Shortest Path, Multiple Knapsack, and Traveling Salesperson Problem, and discussed empirical insights that may guide future research. PyEPO and its documentation are available at https://github.com/khalil-research/PyEPO. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2206_14234 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming Tang, Bo Khalil, Elias B. Optimization and Control Machine Learning 90C05, 90C10, 68T07, 68N30 G.1.6; I.2.6; D.2.2 In deterministic optimization, it is typically assumed that all problem parameters are fixed and known. In practice, however, some parameters may be a priori unknown but can be estimated from contextual information. A typical predict-then-optimize approach separates predictions and optimization into two distinct stages. Recently, end-to-end predict-then-optimize has emerged as an attractive alternative. This work introduces the PyEPO package, a PyTorch-based end-to-end predict-then-optimize library in Python. To the best of our knowledge, PyEPO (pronounced like \textit{pineapple} with a silent ``n") is the first such generic tool for linear and integer programming with predicted objective function coefficients. It includes various algorithms such as surrogate decision losses, black-box solvers, and perturbed methods. PyEPO offers a user-friendly interface for defining new optimization problems, applying state-of-the-art algorithms, and using custom neural network architectures. We conducted experiments comparing various methods on problems such as Shortest Path, Multiple Knapsack, and Traveling Salesperson Problem, and discussed empirical insights that may guide future research. PyEPO and its documentation are available at https://github.com/khalil-research/PyEPO. |
| title | PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming |
| topic | Optimization and Control Machine Learning 90C05, 90C10, 68T07, 68N30 G.1.6; I.2.6; D.2.2 |
| url | https://arxiv.org/abs/2206.14234 |