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Справка Scilab >> Linear Algebra > Eigenvalue and Singular Value > svd

svd

singular value decomposition

Calling Sequence

s=svd(X)
[U,S,V]=svd(X)
[U,S,V]=svd(X,0) (obsolete)
[U,S,V]=svd(X,"e")
[U,S,V,rk]=svd(X [,tol])

Arguments

X

a real or complex matrix

s

real vector (singular values)

S

real diagonal matrix (singular values)

U,V

orthogonal or unitary square matrices (singular vectors).

tol

real number

Description

[U,S,V] = svd(X) produces a diagonal matrix S , of the same dimension as X and with nonnegative diagonal elements in decreasing order, and unitary matrices U and V so that X = U*S*V'.

[U,S,V] = svd(X,0) produces the "economy size" decomposition. If X is m-by-n with m > n, then only the first n columns of U are computed and S is n-by-n.

s= svd(X) by itself, returns a vector s containing the singular values.

[U,S,V,rk]=svd(X,tol) gives in addition rk, the numerical rank of X i.e. the number of singular values larger than tol.

The default value of tol is the same as in rank.

Examples

X=rand(4,2)*rand(2,4)
svd(X)
sqrt(spec(X*X'))

See Also

  • rank — rank
  • qr — QR decomposition
  • colcomp — column compression, kernel, nullspace
  • rowcomp — row compression, range
  • sva — singular value approximation
  • spec — eigenvalues of matrices and pencils

Used Functions

svd decompositions are based on the Lapack routines DGESVD for real matrices and ZGESVD for the complex case.

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Last updated:
Thu Oct 02 14:01:04 CEST 2014