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.
USEFUL FOR
Mathematicians, students studying linear algebra, and professionals working in fields requiring matrix analysis, such as data science and engineering.