In a singular value decomposition of matrix \( A = \begin{bmatrix} 6 & -3 & -3 & 0 \\ 0 & -3 & -3 & 6 \end{bmatrix} \) with \( \Sigma = \begin{bmatrix} 8.49 & 0 & 0 & 0 \\ 0 & 6.0 & 0 & 0 \end{bmatrix} \) and \[ V = \begin{bmatrix} 0.5 & -0.71 & 0.41 & 0.0 \\ -0.5 & 0.0 & 0.82 & -0.71 \\ -0.5 & 0.0 & 0.0 & 0.71 \\ 0.5 & 0.71 & 0.41 & 0.0 \end{bmatrix} \]what is \( U \)?This exercise demonstrates the concept of singular value decomposition (SVD), where:- \( A \) is the original matrix,- \( \Sigma \) (Sigma) is the diagonal matrix of singular values,- \( V \) is the matrix of right singular vectors,- \( U \) is the matrix of left singular vectors to be determined. ---Note: There is a placeholder for users to input or calculate the matrix \( U \):\[ U = \begin{bmatrix} \text{Ex: 1.23} & \\ & \end{bmatrix} \]

In a singular value decomposition of matrix A - V = 0.5 -0.71 0.41 -0.5 -0.5 0.5 U = 0.0 0.0 0.82 -0.71 0.0 0.0 0.71 0.71 0.41 0.0 Ex: 1.23 60 -3 -3 07 %] -3 -3 6] what is U? with Σ = 8.49 0 0 6.0 00 00 and

In a singular value decomposition of matrix A - V = 0.5 -0.71 0.41 -0.5 -0.5 0.5 U = 0.0 0.0 0.82 -0.71 0.0 0.0 0.71 0.71 0.41 0.0 Ex: 1.23 60 -3 -3 07 %] -3 -3 6] what is U? with Σ = 8.49 0 0 6.0 00 00 and

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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