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Generalized singular value decomposition

`[U,V,X,C,S] = gsvd(A,B)sigma = gsvd(A,B)`

`[U,V,X,C,S] = gsvd(A,B)` returns
unitary matrices `U` and `V`, a
(usually) square matrix `X`, and nonnegative diagonal
matrices `C` and `S` so that

A = U*C*X' B = V*S*X' C'*C + S'*S = I

`A` and `B` must have the
same number of columns, but may have different numbers of rows. If `A` is `m`-by-`p` and `B` is `n`-by-`p`,
then `U` is `m`-by-`m`, `V` is `n`-by-`n` and `X` is `p`-by-`q` where `q
= min(m+n,p)`.

`sigma = gsvd(A,B)` returns
the vector of generalized singular values, `sqrt(diag(C'*C)./diag(S'*S))`.

The nonzero elements of `S` are always on its
main diagonal. If `m >= p` the nonzero elements
of `C` are also on its main diagonal. But if `m
< p`, the nonzero diagonal of `C` is `diag(C,p-m)`.
This allows the diagonal elements to be ordered so that the generalized
singular values are nondecreasing.

`gsvd(A,B,0)`, with three input arguments and
either `m` or `n >= p`, produces
the "economy-sized"decomposition where the resulting `U` and `V` have
at most `p` columns, and `C` and `S` have
at most `p` rows. The generalized singular values
are `diag(C)./diag(S)`.

When `B` is square and nonsingular, the generalized
singular values, `gsvd(A,B)`, are equal to the ordinary
singular values, `svd(A/B)`, but they are sorted
in the opposite order. Their reciprocals are `gsvd(B,A)`.

In this formulation of the `gsvd`, no assumptions
are made about the individual ranks of `A` or `B`.
The matrix `X` has full rank if and only if the matrix `[A;B]` has
full rank. In fact, `svd(X)` and `cond(X)` are
equal to `svd([A;B])` and `cond([A;B])`.
Other formulations, eg. G. Golub and C. Van Loan [1], require that `null(A)` and `null(B)` do
not overlap and replace `X` by `inv(X)` or `inv(X')`.

Note, however, that when `null(A)` and `null(B)` do
overlap, the nonzero elements of `C` and `S` are
not uniquely determined.

The matrices have at least as many rows as columns.

A = reshape(1:15,5,3) B = magic(3) A = 1 6 11 2 7 12 3 8 13 4 9 14 5 10 15 B = 8 1 6 3 5 7 4 9 2

The statement

[U,V,X,C,S] = gsvd(A,B)

produces a 5-by-5 orthogonal `U`, a 3-by-3
orthogonal `V`, a 3-by-3 nonsingular `X`,

X = 2.8284 -9.3761 -6.9346 -5.6569 -8.3071 -18.3301 2.8284 -7.2381 -29.7256

and

C = 0.0000 0 0 0 0.3155 0 0 0 0.9807 0 0 0 0 0 0 S = 1.0000 0 0 0 0.9489 0 0 0 0.1957

Since `A` is rank deficient, the first diagonal
element of `C` is zero.

The economy sized decomposition,

[U,V,X,C,S] = gsvd(A,B,0)

produces a 5-by-3 matrix `U` and a 3-by-3 matrix `C`.

U = 0.5700 -0.6457 -0.4279 -0.7455 -0.3296 -0.4375 -0.1702 -0.0135 -0.4470 0.2966 0.3026 -0.4566 0.0490 0.6187 -0.4661 C = 0.0000 0 0 0 0.3155 0 0 0 0.9807

The other three matrices, `V`, `X`,
and `S` are the same as those obtained with the full
decomposition.

The generalized singular values are the ratios of the diagonal
elements of `C` and `S`.

sigma = gsvd(A,B) sigma = 0.0000 0.3325 5.0123

These values are a reordering of the ordinary singular values

svd(A/B) ans = 5.0123 0.3325 0.0000

The matrices have at least as many columns as rows.

A = reshape(1:15,3,5) B = magic(5) A = 1 4 7 10 13 2 5 8 11 14 3 6 9 12 15 B = 17 24 1 8 15 23 5 7 14 16 4 6 13 20 22 10 12 19 21 3 11 18 25 2 9

The statement

[U,V,X,C,S] = gsvd(A,B)

produces a 3-by-3 orthogonal `U`, a 5-by-5
orthogonal `V`, a 5-by-5 nonsingular `X` and

C = 0 0 0.0000 0 0 0 0 0 0.0439 0 0 0 0 0 0.7432 S = 1.0000 0 0 0 0 0 1.0000 0 0 0 0 0 1.0000 0 0 0 0 0 0.9990 0 0 0 0 0 0.6690

In this situation, the nonzero diagonal of `C` is `diag(C,2)`.
The generalized singular values include three zeros.

sigma = gsvd(A,B) sigma = 0 0 0.0000 0.0439 1.1109

Reversing the roles of `A` and `B` reciprocates
these values, producing two infinities.

gsvd(B,A) ans = 1.0e+16 * 0.0000 0.0000 8.8252 Inf Inf

The only warning or error message produced by `gsvd` itself
occurs when the two input arguments do not have the same number of
columns.

[1] Golub, Gene H. and Charles Van Loan, *Matrix
Computations*, Third Edition, Johns Hopkins University
Press, Baltimore, 1996

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