Is gradient descent the only way to find the weights in logistic regression? This post: When is logistic regression solved in closed form? describes that we must use nonlinear optimization methods to find the parameter estimates for logistic regression models. Does gradient descent count as one of these methods? I have not heard of gradient descent described as a nonlinear optimization method. And what other "nonlinear optimization" methods are there for calculating the weights in this case?

A logistic regression is a neural network with no hidden layers. Consequently, any method used for calculating the weights in a neural network is fair game for a logistic regression.
