Which vectorial norm should I use?

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SUMMARY

This discussion focuses on selecting an appropriate vectorial norm for evaluating the rate of convergence in iterative methods for nonlinear systems of equations. The formula used is r^{(k)}=\frac{||x^{(k+1)}-x^{(k)}||_V}{||x^{(k)}-x^{(k-1)}||_V}. The infinity norm (||.||_{\infty}) is commonly suggested, although all norms are equivalent in finite-dimensional normed vector spaces, leading to the same topology. The order of convergence can be assessed by the slope of the convergence plot on a logarithmic scale.

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  • Understanding of iterative methods for nonlinear equations
  • Familiarity with vectorial norms, specifically ||.||_{\infty} and ||.||_1
  • Knowledge of convergence analysis techniques
  • Basic principles of normed vector spaces
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  • Research the properties and applications of different vectorial norms in numerical analysis
  • Study the concept of order of convergence and its implications in iterative methods
  • Learn how to create and interpret convergence plots on a logarithmic scale
  • Explore the equivalence of norms in finite-dimensional spaces and their impact on convergence
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Mathematicians, numerical analysts, and researchers working on iterative methods for solving nonlinear equations will benefit from this discussion.

RicardoMP
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I am to study how fast an iterative method for nonlinear system of equations converges to a certain root and found out that I can evaluate my rate of convergence by using the following formula: ##r^{(k)}=\frac{||x^{(k+1)}-x^{(k)}||_V}{||x^{(k)}-x^{(k-1)}||_V}##. My question is which vectorial norm should I use? ##||.||_{\infty}##? ##||.||_1##? Etc... Is there any criteria in choosing them? And after getting my rate of convergence, how do I measure my order of convergence?
 
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I think it is common to use the infinity norm. All the norms should be related by a constant multiple anyway, so when you talk about order, it should not matter too much.
The order of convergence is often displayed as the slope of your convergence plot on a log scale.
 
If you work in a normed vector space with finite dimension, all these norms are equivalent. They give rise to the same topology.
 

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