Tutorial
This quickstart guide aims to demonstrate the fundamental mechanisms for utilizing PyEPO and highlights the modeling capabilities of PyEPO on a diverse set of predict-then-optimize problems.
- Model
- Data
- Two-stage Method
- Auto Grad Functions
- Smart Predict-then-Optimize+ Loss (SPO+)
- Differentiable Black-box Optimizer (DBB)
- Negative Identity Backpropagation (NID)
- Differentiable Perturbed Optimizer (DPO)
- Perturbed Fenchel-Young Loss (PYFL)
- Implicit Maximum Likelihood Estimator (I-MLE)
- Adaptive Implicit Maximum Likelihood Estimator (AI-MLE)
- Noise Contrastive Estimation (NCE)
- Contrastive Maximum A Posterior Estimation (CMAP)
- Learning to Rank (LTR)
- Perturbation Gradient Loss (PG)
- Parallel Computation
- Solution Pool
- Training
- Evaluation