# magni.cs.reconstruction.gamp._config module¶

Module providing configuration options for the magni.cs.reconstruction.gamp subpackage.

Notes

This module instantiates the Configger class provided by magni.cs.reconstruction._config.Configger. The configuration options are the following:

damping : float
The damping applied to the variable side updates (the default is 0.0).
input_channel : magni.utils.validation.types.MMSEInputChannel
The input channel to use (the default is magni.cs.reconstruction.gamp.input_channel.IIDBG).
input_channel_parameters : dict
The parameters used in the input channel (no default is provided, which implies that this must be specified by the user).
iterations : int
The maximum number of iterations to do (the default is 300).
output_channel : magni.utils.validation.types.MMSEOutputChannel
The output channel to use (the default is magni.cs.reconstruction.gamp.output_channel.AWGN).
output_channel_parameters : dict
The parameters used in the output channel (no default is provided, which implies that this must be specified by the user).
precision_float : {np.float, np.float16, np.float32, np.float64, np.float128,
np.complex64, np.complex128, np.complex256} The floating point precision used for the computations (the default is np.float64).
report_A_asq_setup : bool
The indicator of whether or not to print the A_asq details (the default is False).
report_history : bool
The indicator of whether or not to return the progress history along with the result (the default is False).
stop_criterion : magni.utils.validation.types.StopCriterion
The stop criterion to use in the iterations (the default is magni.cs.reconstruction.gamp.stop_criterion.MSEConvergence).
sum_approximation_constant : dict
The method and constant used in a sum approximation of the squared system transform (the default is {‘rangan’: 1.0}, which implies that Rangan’s uniform variance methods with the system matrix adapted ||A||_F^2/(m*n) constant is used).
tolerance : float
The least acceptable stop criterion tolerance to break the interations (the default is 1e-6).
true_solution : ndarray or None
The true solution to allow for tracking the convergence of the algorithm in the artificial setup where the true solution is known a-priori (the default is None, which implies that no true solution tracking is used).
warm_start : list or tuple
The collection containing the initial guess of the solution vector (alpha_bar) and the solution variance vector (alpha_tilde) (the default is None, which implies that alpha_bar is taken to be a vector of zeros and alpha_tilde is taken to be a vector of ones).