Is Adam's optimization susceptible to Local Minima?
All stochastic gradient descent (SGD) optimizers, including Adam, have randomization built and have no guarantees of reaching a global minima. The randomization is a result of training on a sub-sample of the data at each step. There are no guarantees of reaching a global minima because gradient descent optimizers are first-order, iterative optimization techniques.