# Pages du manuel Linux : Fonctions des bibliothèques

zhpgst
reduce a complex Hermitian-definite generalized eigenproblem to standard form, using packed storage
zhpgv
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
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
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
perform the matrix-vector operation y := alpha*A*x + beta*y,
zhpr
perform the hermitian rank 1 operation A := alpha*x*conjg( x' ) + A,
zhpr2
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
compute the solution to a complex system of linear equations A * X = B,
zhpsvx
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
reduce a complex Hermitian matrix A stored in packed form to real symmetric tridiagonal form T by a unitary similarity transformation
zhptrf
compute the factorization of a complex Hermitian packed matrix A using the Bunch-Kaufman diagonal pivoting method
zhptri
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
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
use inverse iteration to find specified right and/or left eigenvectors of a complex upper Hessenberg matrix H
zhseqr
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
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
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
conjugate a complex vector of length N
zlacon
estimate the 1-norm of a square, complex matrix A
zlacp2
copie all or part of a real two-dimensional matrix A to a complex matrix B
zlacpy
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
:= X / Y, where X and Y are complex
zlaed0
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
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
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
compute the eigendecomposition of a 2-by-2 Hermitian matrix [ A B ] [ CONJG(B) C ]
zlags2
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
compute a partial factorization of a complex Hermitian matrix A using the Bunch-Kaufman diagonal pivoting method
zlahqr
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
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
applie one step of incremental condition estimation in its simplest version
zlals0
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.)