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