networkqit.algorithms.optimize.ContinuousOptimizer

class ContinuousOptimizer(A, x0, beta_range, model, **kwargs)

Continuos optimization method of spectral entropy in the continuous approximation S(rho, sigma(E[L])))

__init__(A, x0, beta_range, model, **kwargs)

Initialization method, must provide the observed network in form of adjacency matrix, the initial optimization parameters and the range over which to span \(eta\).

args:

A (numpy.array): The observed adjacency matrix x0 (numpy.array): The initial value of the optimization parameters (also called θ_0) beta_range (numpy.array, list): The values for which to run optimization

Methods

__init__(A, x0, beta_range, model, **kwargs)	Initialization method, must provide the observed network in form of adjacency matrix, the initial optimization parameters and the range over which to span \(eta\).
gradient(x, rho, beta)	This method computes the gradient as
run(**kwargs)	Starts the optimization.
summary([to_dataframe])	A convenience function to summarize all the optimization process, with results of optimization.