Which vectorial norm should I use?

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The discussion focuses on selecting an appropriate vectorial norm for evaluating the convergence rate of an iterative method for nonlinear equations. The formula for convergence rate involves norms, prompting the question of whether to use infinity norm, one norm, or others. It is noted that while the infinity norm is commonly used, all norms in a finite-dimensional vector space are equivalent and yield the same topology. The order of convergence can be measured as the slope of the convergence plot on a logarithmic scale. Ultimately, the choice of norm may not significantly affect the order of convergence due to their equivalence.
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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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