advanced-linear-algebra

SVD versus eigendecomposition

taught

Every matrix has an SVD; only diagonalizable operators have an eigendecomposition -- and even when both exist, singular values and eigenvalues generally differ, coinciding only for positive semidefinite symmetric matrices.

Taught in

SVD versus eigendecomposition
Advanced Linear Algebra

Requires

svd-geometry-rotate-stretch-rotate required
the-real-spectral-theorem required

Required by

The four fundamental subspaces read off the SVD required

Local knowledge map

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