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arXiv 1711.02935math.APcs.NAmath.NA
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BDF2方法的变分公式用于度量梯度流

A Variational Formulation of the BDF2 Method for Metric Gradient Flows

Technische Universität München(慕尼黑工业大学)

Daniel Matthes, Simon Plazotta

AI总结:

本文提出BDF2方法的变分形式,用于替代常用最小化运动方案,研究度量空间中梯度流的时间离散近似。在假设增广能量泛函均匀半凸的条件下,证明了方法的良定性和离散近似收敛到最速下降曲线。在光滑Hilbert空间中,经典理论预测时间收敛阶为二,但本文在一般度量设置下证明收敛阶为一半。

摘要:

我们提出BDF2方法的变分形式,作为替代常用最小化运动方案,用于抽象度量空间中梯度流的时间离散近似。假设增广能量泛函的均匀半凸性(但不光滑),我们证明了该方法的良定性,并证明离散近似收敛到最速下降曲线。在光滑Hilbert空间中,经典理论预测时间收敛阶为二,但本文在一般度量设置下,在我们的弱假设下证明收敛阶为一半。进一步,我们通过梯度流在紧致黎曼流形、Hilbert空间和L^2-Wasserstein度量上的数值实验来说明这些结果。

英文摘要:

We propose a variational form of the BDF2 method as an alternative to the commonly used minimizing movement scheme for the time-discrete approximation of gradient flows in abstract metric spaces. Assuming uniform semi-convexity --- but no smoothness --- of the augmented energy functional, we prove well-posedness of the method and convergence of the discrete approximations to a curve of steepest descent. In a smooth Hilbertian setting, classical theory would predict a convergence order of two in time, we prove convergence order of one-half in the general metric setting and under our weak hypotheses. Further, we illustrate these results with numerical experiments for gradient flows on a compact Riemannian manifold, in a Hilbert space, and in the $L^2$-Wasserstein metric.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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