1 research outputs found
Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates
With a weighting scheme proportional to t, a traditional stochastic gradient
descent (SGD) algorithm achieves a high probability convergence rate of
O({\kappa}/T) for strongly convex functions, instead of O({\kappa} ln(T)/T). We
also prove that an accelerated SGD algorithm also achieves a rate of
O({\kappa}/T)