Optimal convergence rates for inexact Newton regularization with CG as inner iteration
Optimal convergence rates for inexact Newton regularization with CG as inner iteration
复制标题
以CG作为内迭代的不精确牛顿正则化的最优收敛率
DOI：
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发表时间：
2020
影响因子：
1.1
通讯作者：
A. Neubauer
中科院分区：
文献类型：
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作者：
A. Neubauer
本文证明了一种不精确牛顿正则化方法的阶优性，其中线性化方程用共轭梯度法近似求解。通过差异原理停止外部迭代和内部迭代。我们证明了对于某一参数辨识问题，收敛速度所需的条件是满足的。
Abstract In this paper we prove order optimality of an inexact Newton regularization method, where the linearized equations are solved approximately using the conjugate gradient method. The outer and inner iterations are stopped via the discrepancy principle. We show that the conditions needed for convergence rates are satisfied for a certain parameter identification problem.