- zhpgst
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reduce a complex Hermitian-definite generalized eigenproblem to standard form, using packed storage
- zhpgv
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compute all the eigenvalues and, optionally, the eigenvectors of a complex generalized Hermitian-definite eigenproblem, of the form A*x=(lambda)*B*x, A*Bx=(lambda)*x, or B*A*x=(lambda)*x
- zhpgvd
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compute all the eigenvalues and, optionally, the eigenvectors of a complex generalized Hermitian-definite eigenproblem, of the form A*x=(lambda)*B*x, A*Bx=(lambda)*x, or B*A*x=(lambda)*x
- zhpgvx
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compute selected eigenvalues and, optionally, eigenvectors of a complex generalized Hermitian-definite eigenproblem, of the form A*x=(lambda)*B*x, A*Bx=(lambda)*x, or B*A*x=(lambda)*x
- zhpmv
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perform the matrix-vector operation y := alpha*A*x + beta*y,
- zhpr
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perform the hermitian rank 1 operation A := alpha*x*conjg( x' ) + A,
- zhpr2
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perform the hermitian rank 2 operation A := alpha*x*conjg( y' ) + conjg( alpha )*y*conjg( x' ) + A,
- zhprfs
-
improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian indefinite and packed, and provides error bounds and backward error estimates for the solution
- zhpsv
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compute the solution to a complex system of linear equations A * X = B,
- zhpsvx
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use the diagonal pivoting factorization A = U*D*U**H or A = L*D*L**H to compute the solution to a complex system of linear equations A * X = B, where A is an N-by-N Hermitian matrix stored in packed format and X and B are N-by-NRHS matrices
- zhptrd
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reduce a complex Hermitian matrix A stored in packed form to real symmetric tridiagonal form T by a unitary similarity transformation
- zhptrf
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compute the factorization of a complex Hermitian packed matrix A using the Bunch-Kaufman diagonal pivoting method
- zhptri
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compute the inverse of a complex Hermitian indefinite matrix A in packed storage using the factorization A = U*D*U**H or A = L*D*L**H computed by ZHPTRF
- zhptrs
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solve a system of linear equations A*X = B with a complex Hermitian matrix A stored in packed format using the factorization A = U*D*U**H or A = L*D*L**H computed by ZHPTRF
- zhsein
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use inverse iteration to find specified right and/or left eigenvectors of a complex upper Hessenberg matrix H
- zhseqr
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compute the eigenvalues of a complex upper Hessenberg matrix H, and, optionally, the matrices T and Z from the Schur decomposition H = Z T Z**H, where T is an upper triangular matrix (the Schur form), and Z is the unitary matrix of Schur vectors
- Zim
-
The application object for zim
- Zim::Components::PageView
-
Page TextView widgets
- Zim::Components::PathBar
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Path bar widgets
- Zim::Components::TrayIcon
-
TrayIcon widget for zim
- Zim::Components::TreeView
-
Page index widgets
- Zim::Formats::Html
-
Html dumper for zim
- Zim::Formats::Pod
-
simple module
- Zim::Formats::Wiki
-
Wiki text parser
- Zim::History
-
History object for zim
- Zim::Page
-
Page object for Zim
- Zim::Repository
-
A wiki repository
- Zim::Repository::Man
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Man page repository for zim
- zlabrd
-
reduce the first NB rows and columns of a complex general m by n matrix A to upper or lower real bidiagonal form by a unitary transformation Q' * A * P, and returns the matrices X and Y which are needed to apply the transformation to the unreduced part of A
- zlacgv
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conjugate a complex vector of length N
- zlacon
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estimate the 1-norm of a square, complex matrix A
- zlacp2
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copie all or part of a real two-dimensional matrix A to a complex matrix B
- zlacpy
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copie all or part of a two-dimensional matrix A to another matrix B
- zlacrm
-
perform a very simple matrix-matrix multiplication
- zlacrt
-
perform the operation ( c s )( x ) ==> ( x ) ( -s c )( y ) ( y ) where c and s are complex and the vectors x and y are complex
- zladiv
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:= X / Y, where X and Y are complex
- zlaed0
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the divide and conquer method, ZLAED0 computes all eigenvalues of a symmetric tridiagonal matrix which is one diagonal block of those from reducing a dense or band Hermitian matrix and corresponding eigenvectors of the dense or band matrix
- zlaed7
-
compute the updated eigensystem of a diagonal matrix after modification by a rank-one symmetric matrix
- zlaed8
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merge the two sets of eigenvalues together into a single sorted set
- zlaein
-
use inverse iteration to find a right or left eigenvector corresponding to the eigenvalue W of a complex upper Hessenberg matrix H
- zlaesy
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compute the eigendecomposition of a 2-by-2 symmetric matrix ( ( A, B );( B, C ) ) provided the norm of the matrix of eigenvectors is larger than some threshold value
- zlaev2
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compute the eigendecomposition of a 2-by-2 Hermitian matrix [ A B ] [ CONJG(B) C ]
- zlags2
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compute 2-by-2 unitary matrices U, V and Q, such that if ( UPPER ) then U'*A*Q = U'*( A1 A2 )*Q = ( x 0 ) ( 0 A3 ) ( x x ) and V'*B*Q = V'*( B1 B2 )*Q = ( x 0 ) ( 0 B3 ) ( x x ) or if ( .NOT.UPPER ) then U'*A*Q = U'*( A1 0 )*Q = ( x x ) ( A2 A3 ) ( 0 x ) and V'*B*Q = V'*( B1 0 )*Q = ( x x ) ( B2 B3 ) ( 0 x ) where U = ( CSU SNU ), V = ( CSV SNV ),
- zlagtm
-
perform a matrix-vector product of the form B := alpha * A * X + beta * B where A is a tridiagonal matrix of order N, B and X are N by NRHS matrices, and alpha and beta are real scalars, each of which may be 0., 1., or -1
- zlahef
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compute a partial factorization of a complex Hermitian matrix A using the Bunch-Kaufman diagonal pivoting method
- zlahqr
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i an auxiliary routine called by ZHSEQR to update the eigenvalues and Schur decomposition already computed by ZHSEQR, by dealing with the Hessenberg submatrix in rows and columns ILO to IHI
- zlahrd
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reduce the first NB columns of a complex general n-by-(n-k+1) matrix A so that elements below the k-th subdiagonal are zero
- zlaic1
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applie one step of incremental condition estimation in its simplest version
- zlals0
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applie back the multiplying factors of either the left or the right singular vector matrix of a diagonal matrix appended by a row to the right hand side matrix B in solving the least squares problem using the divide-and-conquer SVD approach
- zlalsa
-
i an itermediate step in solving the least squares problem by computing the SVD of the coefficient matrix in compact form (The singular vectors are computed as products of simple orthorgonal matrices.)