networkqit.algorithms.optimize.Adam

class Adam(G: autograd.numpy.numpy_wrapper.array, x0: autograd.numpy.numpy_wrapper.array, model: networkqit.graphtheory.models.GraphModel.GraphModel, **kwargs)

Implements the ADAM stochastic gradient descent. Adam: A Method for Stochastic Optimization Diederik P. Kingma, Jimmy Ba

https://arxiv.org/abs/1412.6980 However here we use quasi-hyperbolic adam by default, with parameters nu1,nu2 https://arxiv.org/pdf/1810.06801.pdf

__init__(G: autograd.numpy.numpy_wrapper.array, x0: autograd.numpy.numpy_wrapper.array, model: networkqit.graphtheory.models.GraphModel.GraphModel, **kwargs)

Initialize the stochastic optimizer with the observed graph, an initial guess and the model to optimize.

Args:

G (numpy.array) :is the empirical network to study. A N x N adjacency matrix as numpy.array. x0 (numpy.array): is the k-element array of initial parameters estimates. Typically set as random. model (nq.GraphModel): the graph model to optimize the likelihood of.

Methods

__init__(G, x0, model, **kwargs)	Initialize the stochastic optimizer with the observed graph, an initial guess and the model to optimize.
gradient(x, rho, beta[, batch_size])
run(beta[, rho, maxiter, learning_rate, …])