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
norm(x + y) <= norm(x) + norm(y)for a number of input vectors
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|File Name & Checksum SHA256 Checksum Help||Version||File Type||Upload Date|
|odl-0.6.0-py2.py3-none-any.whl (544.4 kB) Copy SHA256 Checksum SHA256||py2.py3||Wheel||Apr 20, 2017|
|odl-0.6.0.tar.gz (434.4 kB) Copy SHA256 Checksum SHA256||–||Source||Apr 20, 2017|