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We propose a first-order method for stochastic strongly convex optimizationthat attains $O(1/n)$ rate of convergence, analysis show that the proposedmethod is simple, easily to implement, and in worst case, asymptotically fourtimes faster than its peers. We derive this method from several intuitiveobservations that are generalized from existing first order optimizationmethods.

Step size adaptation in first-order method for stochastic strongly convex programming