bartorch.optim.IST

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bartorch.optim.IST#

class bartorch.optim.IST(regularizers=None, *, maxiter=30, step=0.95, eigen=False, hogwild=False, cclambda=0.0, precond=None)#

Iterative soft thresholding (pics --ist).

Parameters:

  • regularizers (Regularizer or iterable of Regularizer, optional) – Terms from bartorch.prox.

  • maxiter (int)

  • step (float) – Step size (pics -s); 0.95 is what pics uses when none is given.

  • eigen (bool) – Scale the step by the largest eigenvalue of the normal operator, estimated with 30 power iterations (pics -e).

  • hogwild (bool) – BART’s hogwild setting (pics -H).

  • cclambda (float) – Weight of an identity added to the normal operator (pics -q).

__init__(regularizers=None, *, maxiter=30, step=0.95, eigen=False, hogwild=False, cclambda=0.0, precond=None)#

Methods

__init__([regularizers, maxiter, step, ...])

fixed_point(image_shape, *[, trainable])

This solver as a deep-equilibrium model: the step's fixed point.

in_library(y, A[, x0])

Solve with BART's own loop, without crossing back into Python.

unrolled(image_shape, *[, trainable])

This solver as a network of maxiter steps, trained end to end.
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