and the SUD of the following am A= 11 94040 220

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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**Problem Statement:**

Find the Singular Value Decomposition (SVD) of the following matrix:

\[
A = \begin{bmatrix} 1 & 1 \\ 0 & 1 \end{bmatrix}
\]

**Explanation:**

In this exercise, we aim to determine the Singular Value Decomposition (SVD) of the matrix \( A \). SVD is a method in linear algebra where a matrix \( A \) is decomposed into three matrices \( U \), \( \Sigma \), and \( V^T \) such that:

\[
A = U \Sigma V^T
\]

- \( U \) is an orthogonal matrix.
- \( \Sigma \) is a diagonal matrix with non-negative real numbers known as singular values.
- \( V^T \) is the transpose of an orthogonal matrix \( V \).
Transcribed Image Text:**Problem Statement:** Find the Singular Value Decomposition (SVD) of the following matrix: \[ A = \begin{bmatrix} 1 & 1 \\ 0 & 1 \end{bmatrix} \] **Explanation:** In this exercise, we aim to determine the Singular Value Decomposition (SVD) of the matrix \( A \). SVD is a method in linear algebra where a matrix \( A \) is decomposed into three matrices \( U \), \( \Sigma \), and \( V^T \) such that: \[ A = U \Sigma V^T \] - \( U \) is an orthogonal matrix. - \( \Sigma \) is a diagonal matrix with non-negative real numbers known as singular values. - \( V^T \) is the transpose of an orthogonal matrix \( V \).
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