pyepo 2.2.3

PyTorch-based End-to-End Predict-then-Optimize Tool

PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Tool

PyEPO (PyTorch-based End-to-End Predict-then-Optimize Tool) is a Python-based, open-source software that supports modeling and solving predict-then-optimize problems with linear objective functions. The core capability of PyEPO is to build optimization models with GurobiPy, COPT, Pyomo, Google OR-Tools, MPAX, or any other solvers and algorithms, then embed the optimization model into an artificial neural network for the end-to-end training. For this purpose, PyEPO implements various methods as PyTorch autograd modules.

Features

• Implement SPO+, PG, DPO (additive and multiplicative perturbations), PFYL (additive and multiplicative perturbations), I-MLE, AI-MLE, L2-regularized RFWO/RFYL, DBB, NID, CaVE, NCE, and LTR
• Support Gurobi, COPT, Pyomo, Google OR-Tools, and MPAX API
• Symbolic modeling with pyepo.dsl: define an LP, MIP, or QP once, then compile it to any backend
• JAX frontend (pyepo.func.jax): train any loss in JAX/Flax with jax.grad
• Support parallel computing for optimization solvers
• Support solution caching to speed up training
• Support kNN robust loss to improve decision quality

CaVE for Binary Linear Programs

For end-to-end learning on binary linear programs (TSP, CVRP, knapsack, ...), PyEPO ships CaVE. CaVE replaces the per-step ILP solve with a cone-alignment projection onto the binding-constraint normals at the true optimum, backed by an interior-point QP solver (Clarabel). Because the cone projection is far cheaper than the per-instance ILP solve, CaVE trains an order of magnitude faster than SPO+ at TSP scale.

GPU-Accelerated Solving with MPAX

PyEPO integrates MPAX, a JAX-based mathematical programming solver using the PDHG algorithm for GPU-accelerated optimization. Key advantages: (1) GPU-native solving — the first-order PDHG method runs efficiently on GPU; (2) batch solving — an entire mini-batch can be solved simultaneously via vectorization; (3) no GPU-CPU data transfer overhead — both the neural network and the solver stay on GPU, eliminating the data transfer bottleneck.

Documentation

The official docs can be found at https://khalil-research.github.io/PyEPO.

Publication

PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming (Mathematical Programming Computation)