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A singular value and corresponding singular vectors of a rectangular matrix A are, respectively, a scalar σ and a pair of vectors u and v that satisfy
Av = σu
A′u = σv.
With the singular values on the diagonal of a diagonal matrix Σ and the corresponding singular vectors forming the columns of two orthogonal matrices U and V, you have
AV = UΣ
A′U = VΣ.
Since U and V are orthogonal, this becomes the singular value decomposition
A = UΣV′.
The full singular value decomposition of an m-by-n matrix involves an m-by-m U, an m-by-n Σ, and an n-by-n V. In other words, U and V are both square and Σ is the same size as A. If A has many more rows than columns, the resulting U can be quite large, but most of its columns are multiplied by zeros in Σ. In this situation, the economy sized decomposition saves both time and storage by producing an m-by-n U, an n-by-n Σ and the same V.
The eigenvalue decomposition is the appropriate tool for analyzing a matrix when it represents a mapping from a vector space into itself, as it does for an ordinary differential equation. However, the singular value decomposition is the appropriate tool for analyzing a mapping from one vector space into another vector space, possibly with a different dimension. Most systems of simultaneous linear equations fall into this second category.
If A is square, symmetric, and positive definite, then its eigenvalue and singular value decompositions are the same. But, as A departs from symmetry and positive definiteness, the difference between the two decompositions increases. In particular, the singular value decomposition of a real matrix is always real, but the eigenvalue decomposition of a real, nonsymmetric matrix might be complex.
For the example matrix
A = 9 4 6 8 2 7
the full singular value decomposition is
[U,S,V] = svd(A) U = 0.6105 -0.7174 0.3355 0.6646 0.2336 -0.7098 0.4308 0.6563 0.6194 S = 14.9359 0 0 5.1883 0 0 V = 0.6925 -0.7214 0.7214 0.6925
You can verify that U*S*V' is equal to A to within round-off error. For this small problem, the economy size decomposition is only slightly smaller:
[U,S,V] = svd(A,0) U = 0.6105 -0.7174 0.6646 0.2336 0.4308 0.6563 S = 14.9359 0 0 5.1883 V = 0.6925 -0.7214 0.7214 0.6925
Again, U*S*V' is equal to A to within round-off error.