LAPACK Singular Value and Eigenvalue Problem Routines#
LAPACK Singular Value and Eigenvalue Problem routines are used for singular value and eigenvalue problems, and for performing a number of related computational tasks. The following table lists the LAPACK Singular Value and Eigenvalue Problem routine groups.
Routines |
Scratchpad Size Routines |
Description |
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Reduces a general matrix to bidiagonal form. |
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Computes the singular value decomposition of a general rectangular matrix. |
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Computes all eigenvalues and, optionally, all eigenvectors of a complex Hermitian matrix using divide and conquer algorithm. |
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Computes all eigenvalues and, optionally, all eigenvectors of a complex generalized Hermitian definite eigenproblem using divide and conquer algorithm. |
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Reduces a complex Hermitian matrix to tridiagonal form. |
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Generates the real orthogonal matrix \(Q\) or \(P^T\) determined by gebrd. |
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Generates the real orthogonal matrix \(Q\) determined by sytrd. |
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Multiplies a real matrix by the orthogonal matrix \(Q\) determined by sytrd. |
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Computes all eigenvalues and, optionally, all eigenvectors of a real symmetric matrix using divide and conquer algorithm. |
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Computes all eigenvalues and, optionally, all eigenvectors of a real generalized symmetric definite eigenproblem using divide and conquer algorithm. |
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Reduces a real symmetric matrix to tridiagonal form. |
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Generates the complex unitary matrix \(Q\) or \(P^T\) determined by gebrd. |
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Generates the complex unitary matrix \(Q\) determined by hetrd. |
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Multiplies a complex matrix by the unitary matrix \(Q\) determined by hetrd. |