Since the log-likelihood function is concave, the method of steepest ascent is guaranteed to find the global maximum.
we can adapt GIS to estimate the model parameters θ rather than the model probabilities q, yielding the update rule: The step size, and thus the rate of convergence, depends on the constant C: the larger the value of C, the smaller the step size.