What is the Maximum Norm Proof for Matrix A?

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    Maximum Norm Proof
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Discussion Overview

The discussion revolves around proving the maximum norm for a matrix \( A \in \mathbb{R}^{n \times n} \), specifically that \( ||A||_{\infty} = \text{max}_{i=1,...,n} \sum_{j=1}^n |a_{ij}| \). Participants explore various approaches and definitions related to the infinity norm, including connections to the \( p \)-norm and alternative formulations.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • Some participants suggest starting with the \( ||A||_p \) norm and taking the limit as \( p \to \infty \) to prove the maximum norm.
  • There is a contention regarding the definition of the infinity norm, with some arguing it is the maximum of \( |a_i| \) rather than the sum of them.
  • One participant proposes using the equivalent definition \( ||A||_{\infty} = \max \{ ||Ax||_{\infty} : ||x||_{\infty} \leqslant 1 \} \) and outlines a method to show the inequality involving \( ||Ax||_{\infty} \) and \( \sum_{j=1}^n |a_{ij}| \).
  • Another participant suggests finding \( ||Ax|| \) for a specific vector \( x \) to demonstrate the reverse inequality.

Areas of Agreement / Disagreement

Participants express differing views on the definition and implications of the infinity norm, indicating that multiple competing interpretations and approaches remain in the discussion.

Contextual Notes

Some assumptions regarding the definitions of norms and the conditions under which they apply are not fully articulated, leading to potential ambiguity in the arguments presented.

Amer
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Prove that for

$A \in \mathbb{R}^{n\times n} $
||A||_{\infty} = \text{max}_{i=1,...,n} \sum_{j=1}^n |a_{ij} |

I know that
$||A||_{\infty} = \text{max} \dfrac{||Ax||_{\infty} }{||x||_{\infty}} $

such that $x \in \mathbb{R}^n$

any hints
 
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Amer said:
Prove that for

$A \in \mathbb{R}^{n\times n} $
||A||_{\infty} = \text{max}_{i=1,...,n} \sum_{j=1}^n |a_{ij} |

I know that
$||A||_{\infty} = \text{max} \dfrac{||Ax||_{\infty} }{||x||_{\infty}} $

such that $x \in \mathbb{R}^n$

any hints

I believe you can start with $||A||_p$ the p norm and take the limit as $p\to\infty$ to prove the problem.
Isn't the infinity norm just the max of $|a_i|$ not the sum of them?
 
dwsmith said:
I believe you can start with $||A||_p$ the p norm and take the limit as $p\to\infty$ to prove the problem.
Isn't the infinity norm just the max of $|a_i|$ not the sum of them?
for a vector it is the max of $|a_i|$
 
Amer said:
Prove that for

$A \in \mathbb{R}^{n\times n} $
||A||_{\infty} = \text{max}_{i=1,...,n} \sum_{j=1}^n |a_{ij} |

I know that
$||A||_{\infty} = \text{max} \dfrac{||Ax||_{\infty} }{||x||_{\infty}} $

such that $x \in \mathbb{R}^n$

any hints
You might find it easier to use the equivalent definition $\|A\|_\infty = \max \{\|Ax\|_\infty : \|x\|_\infty \leqslant 1\}.$ For $\|x\|_\infty \leqslant 1$, show that $$\|Ax\|_\infty = \max_{1\leqslant i\leqslant n}|(Ax)_i| = \max_{1\leqslant i\leqslant n}\Bigl| \sum_{j=1}^n a_{ij}x_j \Bigr| \leqslant \sum_{j=1}^n |a_{ij} |.$$
For the reverse inequality, find $\|Ax\|$ where $x$ is the vector with a 1 in the $i$th coordinate and 0 for every other coordinate.
 

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