This work provides a simplified proof of the statistical minimax

optimality of (iterate averaged) stochastic gradient descent (SGD), for

the special case of least squares. This result is obtained by

analyzing SGD as a stochastic process and by sharply characterizing

the stationary covariance matrix of this process. The finite rate optimality characterization captures the

constant factors and addresses model mis-specification.