Operator Discretization Library
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
- Different problems can be solved with the same method (e.g. TV regularization) by simply switching operator and data.
- The same problem can be solved with different methods by simply calling into different solvers.
- Solvers and application-specific code need to be written only once, in one place, and can be tested individually.
- 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.
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|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|