# Pages du manuel Linux : Fonctions des bibliothèques

pcdttrsv
solve a tridiagonal triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pcgbsv
solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pcgbtrf
compute a LU factorization of an N-by-N complex banded distributed matrix with bandwidth BWL, BWU
pcgbtrs
solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)
pcgebd2
reduce a complex general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) to upper or lower bidiagonal form B by an unitary transformation
pcgebrd
reduce a complex general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) to upper or lower bidiagonal form B by an unitary transformation
pcgecon
estimate the reciprocal of the condition number of a general distributed complex matrix A(IA:IA+N-1,JA:JA+N-1), in either the 1-norm or the infinity-norm, using the LU factorization computed by PCGETRF
pcgeequ
compute row and column scalings intended to equilibrate an M-by-N distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA:JA+N-1) and reduce its condition number
pcgehd2
reduce a complex general distributed matrix sub( A ) to upper Hessenberg form H by an unitary similarity transformation
pcgehrd
reduce a complex general distributed matrix sub( A ) to upper Hessenberg form H by an unitary similarity transformation
pcgelq2
compute a LQ factorization of a complex distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = L * Q
pcgelqf
compute a LQ factorization of a complex distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = L * Q
pcgels
solve overdetermined or underdetermined complex linear systems involving an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1),
pcgeql2
compute a QL factorization of a complex distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * L
pcgeqlf
compute a QL factorization of a complex distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * L
pcgeqpf
compute a QR factorization with column pivoting of a M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1)
pcgeqr2
compute a QR factorization of a complex distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * R
pcgeqrf
compute a QR factorization of a complex distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * R
pcgerfs
improve the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solutions
pcgerq2
compute a RQ factorization of a complex distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = R * Q
pcgerqf
compute a RQ factorization of a complex distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = R * Q
pcgesv
compute the solution to a complex system of linear equations sub( A ) * X = sub( B ),
pcgesvx
use the LU factorization to compute the solution to a complex system of linear equations A(IA:IA+N-1,JA:JA+N-1) * X = B(IB:IB+N-1,JB:JB+NRHS-1),
pcgetf2
compute an LU factorization of a general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) using partial pivoting with row interchanges
pcgetrf
compute an LU factorization of a general M-by-N distributed matrix sub( A ) = (IA:IA+M-1,JA:JA+N-1) using partial pivoting with row interchanges
pcgetri
compute the inverse of a distributed matrix using the LU factorization computed by PCGETRF
pcgetrs
solve a system of distributed linear equations op( sub( A ) ) * X = sub( B ) with a general N-by-N distributed matrix sub( A ) using the LU factorization computed by PCGETRF
pcggqrf
compute a generalized QR factorization of an N-by-M matrix sub( A ) = A(IA:IA+N-1,JA:JA+M-1) and an N-by-P matrix sub( B ) = B(IB:IB+N-1,JB:JB+P-1)
pcggrqf
compute a generalized RQ factorization of an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1)
pcheevx
compute selected eigenvalues and, optionally, eigenvectors of a complex hermitian matrix A by calling the recommended sequence of ScaLAPACK routines
pchegs2
reduce a complex Hermitian-definite generalized eigenproblem to standard form
pchegst
reduce a complex Hermitian-definite generalized eigenproblem to standard form
pchegvx
compute all the eigenvalues, and optionally, the eigenvectors of a complex generalized Hermitian-definite eigenproblem
pchetd2
reduce a complex Hermitian matrix sub( A ) to Hermitian tridiagonal form T by an unitary similarity transformation
pchetrd
reduce a complex Hermitian matrix sub( A ) to Hermitian tridiagonal form T by an unitary similarity transformation
pclabrd
reduce the first NB rows and columns of a complex general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) to upper or lower bidiagonal form by an unitary transformation Q' * A * P, and returns the matrices X and Y which are needed to apply the transfor- mation to the unreduced part of sub( A )
pclacgv
conjugate a complex vector of length N, sub( X ), where sub( X ) denotes X(IX,JX:JX+N-1) if INCX = DESCX( M_ ) and X(IX:IX+N-1,JX) if INCX = 1, and Notes ===== Each global data object is described by an associated description vector
pclacon
estimate the 1-norm of a square, complex distributed matrix A
pclacp2
copie all or part of a distributed matrix A to another distributed matrix B
pclacpy
copie all or part of a distributed matrix A to another distributed matrix B
pclaevswp
move the eigenvectors (potentially unsorted) from where they are computed, to a ScaLAPACK standard block cyclic array, sorted so that the corresponding eigenvalues are sorted
pclahrd
reduce the first NB columns of a complex general N-by-(N-K+1) distributed matrix A(IA:IA+N-1,JA:JA+N-K) so that elements below the k-th subdiagonal are zero
pclange
return the value of the one norm, or the Frobenius norm,
pclanhe
return the value of the one norm, or the Frobenius norm,
pclanhs
return the value of the one norm, or the Frobenius norm,
pclansy
return the value of the one norm, or the Frobenius norm,
pclantr
return the value of the one norm, or the Frobenius norm,
pclapiv
applie either P (permutation matrix indicated by IPIV) or inv( P ) to a general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1), resulting in row or column pivoting
pclapv2
applie either P (permutation matrix indicated by IPIV) or inv( P ) to a M-by-N distributed matrix sub( A ) denoting A(IA:IA+M-1,JA:JA+N-1), resulting in row or column pivoting
pclaqge
equilibrate a general M-by-N distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) using the row and scaling factors in the vectors R and C