What is the Maximum Norm Proof for Matrix A?

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    Maximum Norm Proof
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SUMMARY

The maximum norm proof for a matrix \( A \in \mathbb{R}^{n \times n} \) is established as \( ||A||_{\infty} = \text{max}_{i=1,...,n} \sum_{j=1}^n |a_{ij}| \). This is derived using the equivalent definition \( ||A||_{\infty} = \max \{ ||Ax||_{\infty} : ||x||_{\infty} \leq 1 \} \). The proof involves demonstrating that \( ||Ax||_{\infty} \leq \sum_{j=1}^n |a_{ij}| \) for any vector \( x \) constrained by \( ||x||_{\infty} \leq 1 \) and establishing the reverse inequality using a specific choice of \( x \).

PREREQUISITES
  • Understanding of matrix norms, specifically the infinity norm.
  • Familiarity with vector spaces and properties of \( \mathbb{R}^n \).
  • Knowledge of the p-norm and its limits.
  • Basic linear algebra concepts, including matrix-vector multiplication.
NEXT STEPS
  • Study the properties of different matrix norms, focusing on \( ||A||_p \) and its limits.
  • Learn about the implications of the infinity norm in functional analysis.
  • Explore examples of matrix norms in practical applications, such as numerical analysis.
  • Investigate the relationship between matrix norms and eigenvalues.
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Mathematicians, students studying linear algebra, and professionals working in fields requiring matrix analysis, such as data science and engineering.

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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