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On Underdamped Nesterov's Acceleration

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arxiv 2304.14642 v1 pith:MSZEYGT7 submitted 2023-04-28 math.OC cs.LGcs.NAmath.NA

classification math.OCcs.LGcs.NAmath.NA
keywords equationdifferentialcaseconvergenceunderdampedframeworkhigh-resolutionobtained
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abstract

The high-resolution differential equation framework has been proven to be tailor-made for Nesterov's accelerated gradient descent method~(\texttt{NAG}) and its proximal correspondence -- the class of faster iterative shrinkage thresholding algorithms (FISTA). However, the systems of theories is not still complete, since the underdamped case ($r < 2$) has not been included. In this paper, based on the high-resolution differential equation framework, we construct the new Lyapunov functions for the underdamped case, which is motivated by the power of the time $t^{\gamma}$ or the iteration $k^{\gamma}$ in the mixed term. When the momentum parameter $r$ is $2$, the new Lyapunov functions are identical to the previous ones. These new proofs do not only include the convergence rate of the objective value previously obtained according to the low-resolution differential equation framework but also characterize the convergence rate of the minimal gradient norm square. All the convergence rates obtained for the underdamped case are continuously dependent on the parameter $r$. In addition, it is observed that the high-resolution differential equation approximately simulates the convergence behavior of~\texttt{NAG} for the critical case $r=-1$, while the low-resolution differential equation degenerates to the conservative Newton's equation. The high-resolution differential equation framework also theoretically characterizes the convergence rates, which are consistent with that obtained for the underdamped case with $r=-1$.

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Cited by 2 Pith papers

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  1. A Family of Controllable Momentum Coefficients for Forward-Backward Accelerated Algorithms

    math.OC 2025-01 conditional novelty 6.0 of 10

    A family of Nesterov-type methods with power-law momentum achieves controllable O(1/k^{2α}) convergence for strongly convex objectives at the critical step size, including monotone and proximal variants.

  2. Lyapunov Analysis For Monotonically Forward-Backward Accelerated Algorithms

    math.OC 2024-12 conditional novelty 6.0 of 10

    M-NAG and M-FISTA converge linearly under strong convexity, proved with a new kinetic-energy-free Lyapunov function built from a shifted mixed sequence.

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