Nesterov's accelerated gradient method for minimizing a smooth strongly convex function $f$ is known to reduce $f(\x_k)-f(\x^*)$ by a factor of $\eps\in(0,1)$ after $k\ge O(\sqrt{L/\ell}\log(1/\eps))$ iterations, where $\ell,L$ are the two parameters of smooth strong convexity. Furthermore, it is known that this is the best possible complexity in the function-gradient oracle model of computation. The method of linear conjugate gradients (CG) also satisfies the same complexity bound in the special case of strongly convex quadratic functions, but in this special case it is faster than the accelerated gradient method. Despite similarities in the algorithms and their asymptotic convergence rates, the conventional analyses of the two methods are nearly disjoint. The purpose of this note is provide a single quantity that decreases on every step at the correct rate for both algorithms. Our unified bound is based on a potential similar to the potential in Nesterov's original analysis. As a side benefit of this analysis, we provide a direct proof that conjugate gradient converges in $O(\sqrt{L/\ell}\log(1/\eps))$ iterations. In contrast, the traditional indirect proof first establishes this result for the Chebyshev algorithm, and then relies on optimality of conjugate gradient to show that its iterates are at least as good as Chebyshev iterates. To the best of our knowledge, ours is the first direct proof of the convergence rate of linear conjugate gradient in the literature.
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