There exists a differentiable convex potential in R^2 such that the Nesterov ODE converges to the minimizer along a trajectory of infinite path length.
The iterates of Nesterov's accelerated algorithm converge in the critical regimes , year =
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APAPC integrates Nesterov acceleration into primal-dual forward-backward schemes by exploiting dual strong convexity to achieve optimal sublinear and accelerated linear convergence rates.
The accelerated backward-forward method achieves O(1/k²) convergence on convex composite problems and accelerated linear convergence when the smooth component is strongly convex.