  We present an optimal gradient method for smooth strongly convex
optimization. The method is optimal in the sense that its worst-case bound on
the distance to an optimal point exactly matches the lower bound on the oracle
complexity for the class of problems, meaning that no black-box first-order
method can have a better worst-case guarantee without further assumptions on
the class of problems at hand. In addition, we provide a constructive recipe
for obtaining the algorithmic parameters of the method and illustrate that it
can be used for deriving methods for other optimality criteria as well.
