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.

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 are 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.

• 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.

Installing ODL should be as easy as

conda install -c odlgroup odl

or

pip install odl

For more detailed instructions, check out the Installation guide.

The code is compatible with Python 2 and 3 through the future library. It is intended to work on all major platforms (GNU/Linux / Mac / Windows).

• ODL Documentation
• Installation guide
• Getting Started
• Code Examples
• API reference

GPL Version 3 or later. See the LICENSE file.

To contact the developers either write an issue on github or send an email to odl@math.kth.se

• Jonas Adler (@adler-j)
• Holger Kohr (@kohr-h)

• Ozan Öktem (@ozanoktem)
• Kati Niinimäki (@niinimaki)
• Axel Ringh (@aringh)
• Julian Moosmann (@moosmann)
• Chong Chen (@chongchenmath)
• Willem Jan Palenstijn (@wjp)

Development of ODL is financially supported by the Swedish Foundation for Strategic Research as part of the project "Low complexity image reconstruction in medical imaging".

Some development time has also been financed by Elekta.