svd

Overloads

Name	Description
svd(const RealMatrix A) -> Object	Computes the singular value decomposition (SVD) of an n-by-p RealMatrix A as a product A=USV^T where U is a n-by-m unitary, V is a p-by-m unitary, $m = min(n,p)$, and S is an m-by-m real positive diagonal matrix; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively. Singular values are always sorted in decreasing order.. By default, the Jacobi algorithm is used; to use the block divide and conquer (BDC) algorithm instead, provide the option 'algorithm' with value 'bdc'.
svd(const ComplexMatrix A) -> Object	Computes the singular value decomposition (SVD) of an n-by-p ComplexMatrix A as a product A=USV^T where U is a n-by-m unitary, V is a p-by-m unitary, $m = min(n,p)$, and S is an m-by-m real positive diagonal matrix; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively. Singular values are always sorted in decreasing order.. By default, the Jacobi algorithm is used; to use the block divide and conquer (BDC) algorithm instead, provide the option 'algorithm' with value 'bdc'.
svd(const ComplexMatrix A, const Map options) -> Object	Computes the singular value decomposition (SVD) of an n-by-p ComplexMatrix A as a product A=USV^T where U is a n-by-m unitary, V is a p-by-m unitary, $m = min(n,p)$, and S is an m-by-m real positive diagonal matrix; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively. Singular values are always sorted in decreasing order.
svd(const RealMatrix A, const Map options) -> Object	Computes the singular value decomposition (SVD) of an n-by-p RealMatrix A as a product A=USV^T where U is a n-by-m unitary, V is a p-by-m unitary, $m = min(n,p)$, and S is an m-by-m real positive diagonal matrix; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively. Singular values are always sorted in decreasing order.

svd(const RealMatrix A) -> Object

Computes the singular value decomposition (SVD) of an n-by-p RealMatrix A as a product A=USV^T where U is a n-by-m unitary, V is a p-by-m unitary, $m = min(n,p)$, and S is an m-by-m real positive diagonal matrix; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively. Singular values are always sorted in decreasing order.. By default, the Jacobi algorithm is used; to use the block divide and conquer (BDC) algorithm instead, provide the option 'algorithm' with value 'bdc'.

Parameters

• A: The RealMatrix.

svd(const ComplexMatrix A) -> Object

Computes the singular value decomposition (SVD) of an n-by-p ComplexMatrix A as a product A=USV^T where U is a n-by-m unitary, V is a p-by-m unitary, $m = min(n,p)$, and S is an m-by-m real positive diagonal matrix; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively. Singular values are always sorted in decreasing order.. By default, the Jacobi algorithm is used; to use the block divide and conquer (BDC) algorithm instead, provide the option 'algorithm' with value 'bdc'.

Parameters

• A: The ComplexMatrix.

svd(const ComplexMatrix A, const Map options) -> Object

Computes the singular value decomposition (SVD) of an n-by-p ComplexMatrix A as a product A=USV^T where U is a n-by-m unitary, V is a p-by-m unitary, $m = min(n,p)$, and S is an m-by-m real positive diagonal matrix; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively. Singular values are always sorted in decreasing order.

Parameters

• A: The ComplexMatrix.
• options: Map of SVD options
  • algorithm (string): The SVD algorithm to use. Possible values are 'jacobi' (default) and 'bdc' (Bidiagonal Divide and Conquer).

svd(const RealMatrix A, const Map options) -> Object

Computes the singular value decomposition (SVD) of an n-by-p RealMatrix A as a product A=USV^T where U is a n-by-m unitary, V is a p-by-m unitary, $m = min(n,p)$, and S is an m-by-m real positive diagonal matrix; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively. Singular values are always sorted in decreasing order.

Parameters

• A: The RealMatrix.
• options: Map of SVD options
  • algorithm (string): The SVD algorithm to use. Possible values are 'jacobi' (default) and 'bdc' (Bidiagonal Divide and Conquer).