Adaptive Monte Carlo sampling gradient method for optimization
Hui Li Tan · 2017
We present a stochastic gradient descent algorithm with adaptive sampling for the unconstrained optimization problem where the function or the gradient is not directly accessible. We show that the algorithm exhibits global convergence and discuss the work complexity with different choices of predetermined function in the sampling rule.