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in python Grading criteria: code and mathematical correctness. (Use scipy.linalg.svd for the SVD.) a. Define the matrix Edit 13 A= (1 12 1 2 )
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Grading criteria: code and mathematical correctness. (Use scipy.linalg.svd for the SVD.) a. Define the matrix Edit 13 A= (1 12 1 2 ) 2 2 -1 -2] . Compute an SVD of A, then use it to find vectors V ER3 which maximize and minimize the ratio b. Check that the vectors you got in part a are not eigenvectors of A. (There are many valid ways to do this.) In [.. C. Read the Wikipedia entry on normal matrices. Then change one entry of A to obtain a matrix for which the analogue of part a does yield eigenvectors, and do the computation to 14 verify this. Grading criteria: code and mathematical correctness. (Use scipy.linalg.svd for the SVD.) a. Define the matrix Edit 13 A= (1 12 1 2 ) 2 2 -1 -2] . Compute an SVD of A, then use it to find vectors V ER3 which maximize and minimize the ratio b. Check that the vectors you got in part a are not eigenvectors of A. (There are many valid ways to do this.) In [.. C. Read the Wikipedia entry on normal matrices. Then change one entry of A to obtain a matrix for which the analogue of part a does yield eigenvectors, and do the computation to 14 verify thisStep by Step Solution
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