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Singular Value Decomposition
 Syntax `svd(`x```) svd(```x`, `u`, `v```) [```u`, `s`, `v`] = svd(`x```) [```u`, `s`, `v`] = svd(`x`, `full`)` See Also eigen , eigsym , schur , qred , lu , cholesky

Description
Computes the singular values corresponding to the matrix x, where x is real, double-precision, or complex. ``` svd(```x```) ```if this syntax is used, the return value is a column vector containing the singular vectors in descending magnitude order. It has the same type as x and its length is equal to the minimum of the row and column dimension of x. ``` svd(```x`, `u`, `v```) ```if this syntax is used, the return value is the same as described above. In addition, the columns of u are set to the left singular vectors and the columns of v are set to the right singular vectors. The input values of u and v have no effect and their output values have the same type as x. ``` [```u`, `s`, `v`] = svd(`x```) [```u`, `s`, `v`] = svd(`x`, `full```) ```if this syntax is used, the matrices u, and v are as described above. In addition, the diagonal matrix s has the singular values along its diagonal and in descending magnitude order. If the argument full is the logical value true, or if it is not present, the full decomposition is computed. Otherwise the economy decomposition is computed. ``` ```If `m` and `n` are the row and column dimensions of x, there is an `m` by `m` unitary matrix `U`, an `m` by `n` diagonal matrix `S`, and an `n` by `n` unitary matrix `V` such that ```      x = U S conj(V)' ```where `conj(V)'` is the complex conjugate transpose of `V`. In addition, the matrix `S` can be chosen so that its diagonal elements are monotone decreasing in absolute value; i.e., ```      | S    |  >   | S        |         i, i          i+1, i+1 ```In the full decomposition case, if the arguments u and v are present, they are set to the matrices `U` and `V` respectively. If the argument s is present, it is set to the matrix `S`, otherwise the return value of `svd` is equal to the diagonal of `S`. ``` ```In the economy decomposition case, the matrix u is set to the first `min(m, n)` columns of `U`, the matrix s is set to the submatrix defined by the intersection of the first `min(m, n)` rows and columns of `S`, and the matrix v is set to the first `min(m, n)` columns of `V`. Thus it still holds that ```                     '      x = u s conj(v) ```
Example
To find the singular values and vectors of the matrix ```      / 2 1 \      \ 1 2 / ```enter ```      x = {[2., 1.], [1., 2.]}      u = novalue      v = novalue      [u, s, v] = svd(x, u, v)      print s, u ``` which returns ```      {      [ 3 , 0 ]      [ 0 , 1 ]      }      {      [ -0.707107 , -0.707107 ]      [ -0.707107 , 0.707107 ]      } ``` If you continue this example by entering ```      u * s * v' ``` O-Matrix will respond ```      {      [ 2 , 1 ]      [ 1 , 2 ]      } ``` (Note that because `x` is not complex, `v` is not complex and it is not necessary to take the complex conjugate of `v` in the example above.)

Notes