Iterative Algorithms#
Sub-package containing optimization routines.
DeepMR provides optimization routines for compressed sensing and network unfolding.
Optimization Steps#
Operators representing single iterations of classical optimization algorithms.
DeepMR expose these operators as torch.nn
objects to be chained e.g., in unrolled Neural Network architectures
and the corresponding functional versions for standalone usage.
Conjugate Gradient method step. |
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Alternate Direction of Multipliers Method step. |
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Proximal Gradient Method step. |
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Solve inverse problem using Conjugate Gradient method. |
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Solve inverse problem using Alternate Direction of Multipliers Method. |
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Solve inverse problem using Proximal Gradient Method. |
In addition, we provide utils to estimate matrix-free operator properties, such as maximum eigenvalue.
Linop linear algebra#
Use power iteration to calculate the spectral norm of a Linop. |