To solve for the Singular Value Decomposition (SVD) of a matrix, we need to use a specific matrix as a starting point. Let's denote a matrix $A$ and walk through its SVD. Singular Value Decomposition decomposes a matrix $A$ into three matrices:

$A = U \Sigma V^T$

where:

• $U$ is an orthogonal matrix (the left singular vectors),
• $\Sigma$ is a diagonal matrix with non-negative real numbers (the singular values),
• $V^T$ is the transpose of an orthogonal matrix (the right singular vectors).

Please provide the matrix $A$ you'd like to use, or I can create an example matrix to demonstrate the steps for SVD.

Let me know how you'd like to proceed! Would you like to upload a photo, or should I generate an example matrix?