Global convergence of Newton’s method for the regularized p-Stokes equations

Abstract The motion of glaciers can be simulated with the $$\varvec{p}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> -Stokes equations. Up to now, Newton’s method to solve these equations has been analyzed in finite-dimensional settings only. We analyze the problem in infinite dimensions to gain a new viewpoint. We do that by proving global convergence of the infinite-dimensional Newton’s method with Armijo step sizes to the solution of these equations. We only have to add an arbitrarily small diffusion term for this convergence result. We prove that the additional diffusion term only causes minor differences in the solution compared to the original $$\varvec{p}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> -Stokes equations under the assumption of some regularity. Finally, we test our algorithms on two experiments: A reformulation of the experiment ISMIP-HOM $$\varvec{B}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>B</mml:mi> </mml:mrow> </mml:math> without sliding and a block with sliding. For the former, the approximation of exact step sizes for the Picard iteration and exact step sizes and Armijo step sizes for Newton’s method are superior in the experiment compared to the Picard iteration. For the latter experiment, Newton’s method with Armijo step sizes needs many iterations until it converges fast to the solution. Thus, Newton’s method with approximately exact step sizes is better than Armijo step sizes in this experiment.

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Publication Details

Journal
Numerical Algorithms
Published
2024-09-17
DOI
https://doi.org/10.1007/s11075-024-01941-6
Citations
1
Primary Topic
Iterative Methods for Nonlinear Equations
Type
article
Field-Weighted Citation Impact
0.73

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article

Global convergence of Newton’s method for the regularized p-Stokes equations

Niko Schmidt
1 citations
Numerical Algorithms
Iterative Methods for Nonlinear Equations
0.73
article

Global convergence of Newton’s method for the regularized p-Stokes equations

Niko Schmidt
article en
1 citations

Abstract

Abstract The motion of glaciers can be simulated with the $$\varvec{p}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> -Stokes equations. Up to now, Newton’s method to solve these equations has been analyzed in finite-dimensional settings only. We analyze the problem in infinite dimensions to gain a new viewpoint. We do that by proving global convergence of the infinite-dimensional Newton’s method with Armijo step sizes to the solution of these equations. We only have to add an arbitrarily small diffusion term for this convergence result. We prove that the additional diffusion term only causes minor differences in the solution compared to the original $$\varvec{p}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> -Stokes equations under the assumption of some regularity. Finally, we test our algorithms on two experiments: A reformulation of the experiment ISMIP-HOM $$\varvec{B}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>B</mml:mi> </mml:mrow> </mml:math> without sliding and a block with sliding. For the former, the approximation of exact step sizes for the Picard iteration and exact step sizes and Armijo step sizes for Newton’s method are superior in the experiment compared to the Picard iteration. For the latter experiment, Newton’s method with Armijo step sizes needs many iterations until it converges fast to the solution. Thus, Newton’s method with approximately exact step sizes is better than Armijo step sizes in this experiment.

Numerical AlgorithmsVol. 99(4)
Christian-Albrechts-Universität zu Kiel (DE)
Christian-Albrechts-Universität zu Kiel, Bayerische Akademie der Wissenschaften, Universität Bremen
Openalex Percentile: Top 31%
Iterative Methods for Nonlinear Equations
0.73
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