Skip to main content

Operator Discretization Library

Project description

Operator Discretization Library (ODL) is a Python library for fast prototyping focusing on (but not restricted to) inverse problems. ODL is being developed at KTH Royal Institute of Technology, Stockholm, and Centrum Wiskunde & Informatica (CWI), Amsterdam.

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. This is reached by an Operator structure which encapsulates all application-specific parts, and a high-level formulation of solvers which usually expect an operator, data and additional parameters. The main advantages of this approach is that

  1. Different problems can be solved with the same method (e.g. TV regularization) by simply switching operator and data.
  2. The same problem can be solved with different methods by simply calling into different solvers.
  3. Solvers and application-specific code need to be written only once, in one place, and can be tested individually.
  4. Adding new applications or solution methods becomes a much easier task.

Features

  • Efficient and well-tested data containers based on Numpy (default) or CUDA (optional)
  • Objects to represent mathematical notions like vector spaces and operators, including properties as expected from mathematics (inner product, norm, operator composition, …)
  • Convenience functionality for operators like arithmetic, composition, operator matrices etc., which satisfy the known mathematical rules.
  • Out-of-the-box support for frequently used operators like scaling, partial derivative, gradient, Fourier transform etc.
  • A versatile and pluggable library of optimization routines for smooth and non-smooth problems, such as CGLS, BFGS, Chambolle-Pock and Douglas-Rachford splitting.
  • Support for tomographic imaging with a unified geometry representation and bindings to external libraries for efficient computation of projections and back-projections.
  • Standardized tests to validate implementations against expected behavior of the corresponding mathematical object, e.g. if a user-defined norm satisfies norm(x + y) <= norm(x) + norm(y) for a number of input vectors x and y.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
odl-0.7.0-py2.py3-none-any.whl (778.0 kB) Copy SHA256 hash SHA256 Wheel py2.py3 Sep 9, 2018
odl-0.7.0.tar.gz (653.8 kB) Copy SHA256 hash SHA256 Source None Sep 9, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page