What is the difference between Eigenvalue Decomposition (EVD) and Singular Value Decomposition (SVD)? When does SVD behave the same as EVD?
a) EVD requires the matrix to be square, while SVD can be applied to any rectangular matrix.
b) In EVD, the matrix must be diagonalizable, whereas SVD can be applied to any matrix.
c) SVD always exists, but EVD may not exist for all matrices.
d) All of the above.
