A singular value decomposition of a matrix A is as follows: -0.5 0.5 0.5 -0.57 10 01 -0.5 -0.5 0.5 0.5 0 5 0.8 0.61 -0.6 0.8 0.5 0.5 0.5 0.5 0 0 0.5 -0.5 0.5 -0.5 0 0 (a) Find the closest (with respect to the Frobenius norm) matrix of rank 1 to A A1 = (a) Find the Frobenius norm of A - A1. ||A - A1|| p = (c) Find the rank of A rank(A) =

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
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Chapter2: Second-order Linear Odes
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A singular value decomposition of a matrix A is as follows:
-0.5 0.5 0.5 -0.57 10 01
-0.5
-0.5 0.5
0.5
0
5 0.8 0.61
0.5
0.5 0.5 0.5
0 0
-0.6 0.8
0.5
-0.5 0.5 -0.5 0 0
(a) Find the closest (with respect to the Frobenius norm) matrix of rank 1 to A
A1 =
(a) Find the Frobenius norm of A - A1.
||A - A1|| p =
(c) Find the rank of A
rank(A) =
Transcribed Image Text:A singular value decomposition of a matrix A is as follows: -0.5 0.5 0.5 -0.57 10 01 -0.5 -0.5 0.5 0.5 0 5 0.8 0.61 0.5 0.5 0.5 0.5 0 0 -0.6 0.8 0.5 -0.5 0.5 -0.5 0 0 (a) Find the closest (with respect to the Frobenius norm) matrix of rank 1 to A A1 = (a) Find the Frobenius norm of A - A1. ||A - A1|| p = (c) Find the rank of A rank(A) =
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