Welcome to PyTorch-BSF!

Fit smooth, high-dimensional manifolds to your data — from a single GPU to a multi-node cluster.

PyTorch-BSF brings Bézier simplex fitting to PyTorch. A Bézier simplex is a high-dimensional generalization of the Bézier curve: it can model an arbitrarily complex point cloud as a smooth parametric hyper-surface in any number of dimensions. This makes it a natural tool for representing Pareto fronts in multi-objective optimization, interpolating scattered observations, and fitting geometric structures in high-dimensional spaces.

The project is on GitHub.

User guide

• Quickstart
  • Run as an MLflow project
  • Run as a Python package
  • Run as Python code
• Advanced Topics
  • Data Normalization
  • Initial Control Points
  • Partial Training
  • Hardware Acceleration
• What is Bézier simplex fitting?
  • Bezier simplex
  • Bézier simplex fitting
  • Approximation theorem
  • Relation to multi-objective optimization
  • Statistical test for simplicial topology
  • References
• Applications
  • Elastic net model selection
  • Robust portfolio management
  • Distributed smart grids and energy operations
  • Multi-task and federated learning
  • Multi-objective model predictive control
  • Communication systems and routing
  • Supply chain and logistics optimization
  • Medical imaging and radiation therapy
  • Facility location and continuous approximations
• Frequently Asked Questions
  • General Information
  • Mathematical Foundations
  • Practical Usage
  • Troubleshooting
  • References

API reference

• torch_bsf
  • torch_bsf package

Indices and tables

• Index

• Module Index

• Search Page