Operator Discretization Library Documentation

Operator Discretization Library (ODL) is a python library for fast prototyping focusing on (but not restricted to) inverse problems.

The main intent of ODL is to enable mathematicians and applied scientists to use different numerical methods on real-world problems without having to implement all necessary parts from the bottom up. ODL provides some of the most heavily used building blocks for numerical algorithms out of the box, which enables users to focus on real scientific issues.

Getting Started

• Getting Started
  • About ODL
  • Installing ODL
  • Installing ODL using conda
  • Installing ODL using pip
  • Installing ODL from source
  • Installing ODL extensions
  • First steps

Working with ODL

• User’s guide – selected topics
  • Operators
  • Linear spaces
  • Using ODL with NumPy and SciPy
  • Vectorized functions
  • Tomographic acquisition geometries
  • Functional
  • Using ODL with ProxImaL
  • Primal-Dual Hybrid Gradient algorithm
• Mathematical Background
  • Linear Spaces
  • Discretizations
  • Resizing Operators
  • On the different notions of derivative
  • Transformations
  • Solvers

Developer zone

• Contributing to ODL
  • Introduction
  • Contents

Useful facts

• Frequently asked questions
• Glossary
• Release Notes

API Reference

• odl
  • contrib
  • deform
  • diagnostics
  • discr
  • operator
  • phantom
  • set
  • solvers
  • space
  • tomo
  • trafos
  • ufunc_ops
  • util

Indices and tables

• Index

• Module Index

• Search Page