Python Sparse data Analysis Package external MRI plugin.

Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the gallery for the big picture.

mri.optimizers.forward_backward.pogm(gradient_op, linear_op, prox_op, cost_op=None, max_nb_of_iter=300, x_init=None, metric_call_period=5, sigma_bar=0.96, metrics={}, verbose=0)[source]¶

Perform sparse reconstruction using the POGM algorithm.

Parameters

gradient_op : instance of class GradBase

the gradient operator.

linear_op : instance of LinearBase

the linear operator: seek the sparsity, ie. a wavelet transform.

prox_op : instance of ProximityParent

the proximal operator.

cost_op : instance of costObj, (default None)

the cost function used to check for convergence during the optimization.

lambda_init : float, (default 1.0)

initial value for the FISTA step.

max_nb_of_iter : int (optional, default 300)

the maximum number of iterations in the POGM algorithm.

x_init : np.ndarray (optional, default None)

the initial guess of image

metric_call_period : int (default 5)

the period on which the metrics are computed.

metrics : dict (optional, default None)

the list of desired convergence metrics: {‘metric_name’: [@metric, metric_parameter]}. See modopt for the metrics API.

verbose : int (optional, default 0)

the verbosity level.

Returns

x_final : ndarray

the estimated POGM solution.

costs : list of float

the cost function values.

metrics : dict

the requested metrics values during the optimization.