📦 scalapack-doc

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C

  • cdbtf2(3)✨
  • cdbtrf(3)✨
  • cdttrf(3)compute an LU factorization of a complex tridiagonal matrix A using elimination without partial pivoting✨
  • cdttrsv(3)solve one of the systems of equations L * X = B, L**T * X = B, or L**H * X = B,✨
  • cpttrsv(3)solve one of the triangular systems L * X = B, or L**H * X = B,✨

D

  • ddbtf2(3)✨
  • ddbtrf(3)✨
  • ddttrf(3)compute an LU factorization of a complex tridiagonal matrix A using elimination without partial pivoting✨
  • ddttrsv(3)solve one of the systems of equations L * X = B, L**T * X = B, or L**H * X = B,✨
  • dlamsh(3)send multiple shifts through a small (single node) matrix to see how consecutive small subdiagonal elements are modified by subsequent shifts in an effort to maximize the number of bulges that can be…✨
  • dlaref(3)applie one or several Householder reflectors of size 3 to one or two matrices (if column is specified) on either their rows or columns✨
  • dlasorte(3)sort eigenpairs so that real eigenpairs are together and complex are together✨
  • dlasrt2(3)the numbers in D in increasing order (if ID = 'I') or in decreasing order (if ID = 'D' )✨
  • dpttrsv(3)solve one of the triangular systems L**T* X = B, or L * X = B,✨
  • dstein2(3)compute the eigenvectors of a real symmetric tridiagonal matrix T corresponding to specified eigenvalues, using inverse iteration✨
  • dsteqr2(3)i a modified version of LAPACK routine DSTEQR✨

P

  • pcdbsv(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcdbtrf(3)compute a LU factorization of an N-by-N complex banded diagonally dominant-like distributed matrix with bandwidth BWL, BWU✨
  • pcdbtrs(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcdbtrsv(3)solve a banded triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcdtsv(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcdttrf(3)compute a LU factorization of an N-by-N complex tridiagonal diagonally dominant-like distributed matrix A(1:N, JA:JA+N-1)✨
  • pcdttrs(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcdttrsv(3)solve a tridiagonal triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcgbsv(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcgbtrf(3)compute a LU factorization of an N-by-N complex banded distributed matrix with bandwidth BWL, BWU✨
  • pcgbtrs(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcgebd2(3)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(3)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(3)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…✨
  • pcgeequ(3)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(3)reduce a complex general distributed matrix sub( A ) to upper Hessenberg form H by an unitary similarity transformation✨
  • pcgehrd(3)reduce a complex general distributed matrix sub( A ) to upper Hessenberg form H by an unitary similarity transformation✨
  • pcgelq2(3)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(3)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(3)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(3)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(3)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(3)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(3)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(3)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(3)improve the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solutions✨
  • pcgerq2(3)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(3)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(3)compute the solution to a complex system of linear equations sub( A ) * X = sub( B ),✨
  • pcgesvx(3)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(3)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(3)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(3)compute the inverse of a distributed matrix using the LU factorization computed by PCGETRF✨
  • pcgetrs(3)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(3)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(3)compute a generalized RQ factorization of an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1)✨
  • pcheevx(3)compute selected eigenvalues and, optionally, eigenvectors of a complex hermitian matrix A by calling the recommended sequence of ScaLAPACK routines✨
  • pchegs2(3)reduce a complex Hermitian-definite generalized eigenproblem to standard form✨
  • pchegst(3)reduce a complex Hermitian-definite generalized eigenproblem to standard form✨
  • pchegvx(3)compute all the eigenvalues, and optionally, the eigenvectors of a complex generalized Hermitian-definite eigenproblem✨
  • pchetd2(3)reduce a complex Hermitian matrix sub( A ) to Hermitian tridiagonal form T by an unitary similarity transformation✨
  • pchetrd(3)reduce a complex Hermitian matrix sub( A ) to Hermitian tridiagonal form T by an unitary similarity transformation✨
  • pclabrd(3)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…✨
  • pclacgv(3)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…✨
  • pclacon(3)estimate the 1-norm of a square, complex distributed matrix A✨
  • pclacp2(3)copie all or part of a distributed matrix A to another distributed matrix B✨
  • pclacpy(3)copie all or part of a distributed matrix A to another distributed matrix B✨
  • pclaevswp(3)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(3)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(3)return the value of the one norm, or the Frobenius norm,✨
  • pclanhe(3)return the value of the one norm, or the Frobenius norm,✨
  • pclanhs(3)return the value of the one norm, or the Frobenius norm,✨
  • pclansy(3)return the value of the one norm, or the Frobenius norm,✨
  • pclantr(3)return the value of the one norm, or the Frobenius norm,✨
  • pclapiv(3)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(3)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(3)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✨
  • pclaqsy(3)equilibrate a symmetric distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) using the scaling factors in the vectors SR and SC✨
  • pclarf(3)applie a complex elementary reflector Q to a complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1), from either the left or the right✨
  • pclarfb(3)applie a complex block reflector Q or its conjugate transpose Q**H to a complex M-by-N distributed matrix sub( C ) denoting C(IC:IC+M-1,JC:JC+N-1), from the left or the right✨
  • pclarfc(3)applie a complex elementary reflector Q**H to a complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1),✨
  • pclarfg(3)generate a complex elementary reflector H of order n, such that H * sub( X ) = H * ( x(iax,jax) ) = ( alpha ), H' * H = I✨
  • pclarft(3)form the triangular factor T of a complex block reflector H of order n, which is defined as a product of k elementary reflectors✨
  • pclarz(3)applie a complex elementary reflector Q to a complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1), from either the left or the right✨
  • pclarzb(3)applie a complex block reflector Q or its conjugate transpose Q**H to a complex M-by-N distributed matrix sub( C ) denoting C(IC:IC+M-1,JC:JC+N-1), from the left or the right✨
  • pclarzc(3)applie a complex elementary reflector Q**H to a complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1),✨
  • pclarzt(3)form the triangular factor T of a complex block reflector H of order > n, which is defined as a product of k elementary reflectors as returned by PCTZRZF✨
  • pclascl(3)multiplie the M-by-N complex distributed matrix sub( A ) denoting A(IA:IA+M-1,JA:JA+N-1) by the real scalar CTO/CFROM✨
  • pclase2(3)initialize an M-by-N distributed matrix sub( A ) denoting A(IA:IA+M-1,JA:JA+N-1) to BETA on the diagonal and ALPHA on the offdiagonals✨
  • pclaset(3)initialize an M-by-N distributed matrix sub( A ) denoting A(IA:IA+M-1,JA:JA+N-1) to BETA on the diagonal and ALPHA on the offdiagonals✨
  • pclassq(3)return the values scl and smsq such that ( scl**2 )*smsq = x( 1 )**2 +...+ x( n )**2 + ( scale**2 )*sumsq,✨
  • pclaswp(3)perform a series of row or column interchanges on the distributed matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1)✨
  • pclatra(3)compute the trace of an N-by-N distributed matrix sub( A ) denoting A( IA:IA+N-1, JA:JA+N-1 )✨
  • pclatrd(3)reduce NB rows and columns of a complex Hermitian distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) to complex tridiagonal form by an unitary similarity transformation Q' * sub( A ) * Q, and…✨
  • pclatrs(3)solve a triangular system✨
  • pclatrz(3)reduce the M-by-N ( M<=N ) complex upper trapezoidal matrix sub( A ) = [A(IA:IA+M-1,JA:JA+M-1) A(IA:IA+M-1,JA+N-L:JA+N-1)]✨
  • pclauu2(3)compute the product U * U' or L' * L, where the triangular factor U or L is stored in the upper or lower triangular part of the matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1)✨
  • pclauum(3)compute the product U * U' or L' * L, where the triangular factor U or L is stored in the upper or lower triangular part of the distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1)✨
  • pcmax1(3)compute the global index of the maximum element in absolute value of a distributed vector sub( X )✨
  • pcpbsv(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcpbtrf(3)compute a Cholesky factorization of an N-by-N complex banded symmetric positive definite distributed matrix with bandwidth BW✨
  • pcpbtrs(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcpbtrsv(3)solve a banded triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcpocon(3)estimate the reciprocal of the condition number (in the 1-norm) of a complex Hermitian positive definite distributed matrix using the Cholesky factorization A = U**H*U or A = L*L**H computed by…✨
  • pcpoequ(3)compute row and column scalings intended to equilibrate a distributed Hermitian positive definite matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) and reduce its condition number (with respect to the…✨
  • pcporfs(3)improve the computed solution to a system of linear equations when the coefficient matrix is Hermitian positive definite and provides error bounds and backward error estimates for the solutions✨
  • pcposv(3)compute the solution to a complex system of linear equations sub( A ) * X = sub( B ),✨
  • pcposvx(3)use the Cholesky factorization A = U**H*U or A = L*L**H 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),✨
  • pcpotf2(3)compute the Cholesky factorization of a complex hermitian positive definite distributed matrix sub( A )=A(IA:IA+N-1,JA:JA+N-1)✨
  • pcpotrf(3)compute the Cholesky factorization of an N-by-N complex hermitian positive definite distributed matrix sub( A ) denoting A(IA:IA+N-1, JA:JA+N-1)✨
  • pcpotri(3)compute the inverse of a complex Hermitian positive definite distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) using the Cholesky factorization sub( A ) = U**H*U or L*L**H computed by PCPOTRF✨
  • pcpotrs(3)solve a system of linear equations sub( A ) * X = sub( B ) A(IA:IA+N-1,JA:JA+N-1)*X = B(IB:IB+N-1,JB:JB+NRHS-1)✨
  • pcptsv(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcpttrf(3)compute a Cholesky factorization of an N-by-N complex tridiagonal symmetric positive definite distributed matrix A(1:N, JA:JA+N-1)✨
  • pcpttrs(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcpttrsv(3)solve a tridiagonal triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pcsrscl(3)multiplie an N-element complex distributed vector sub( X ) by the real scalar 1/a✨
  • pcstein(3)compute the eigenvectors of a symmetric tridiagonal matrix in parallel, using inverse iteration✨
  • pctrcon(3)estimate the reciprocal of the condition number of a triangular distributed matrix A(IA:IA+N-1,JA:JA+N-1), in either the 1-norm or the infinity-norm✨
  • pctrrfs(3)provide error bounds and backward error estimates for the solution to a system of linear equations with a triangular coefficient matrix✨
  • pctrti2(3)compute the inverse of a complex upper or lower triangular block matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1)✨
  • pctrtri(3)compute the inverse of a upper or lower triangular distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1)✨
  • pctrtrs(3)solve a triangular system of the form sub( A ) * X = sub( B ) or sub( A )**T * X = sub( B ) or sub( A )**H * X = sub( B ),✨
  • pctzrzf(3)reduce the M-by-N ( M<=N ) complex upper trapezoidal matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) to upper triangular form by means of unitary transformations✨
  • pcung2l(3)generate an M-by-N complex distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal columns, which is defined as the last N columns of a product of K elementary reflectors of order M Q =…✨
  • pcung2r(3)generate an M-by-N complex distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal columns, which is defined as the first N columns of a product of K elementary reflectors of order M Q…✨
  • pcungl2(3)generate an M-by-N complex distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal rows, which is defined as the first M rows of a product of K elementary reflectors of order N Q = H(k)'✨
  • pcunglq(3)generate an M-by-N complex distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal rows, which is defined as the first M rows of a product of K elementary reflectors of order N Q = H(k)'✨
  • pcungql(3)generate an M-by-N complex distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal columns, which is defined as the last N columns of a product of K elementary reflectors of order M Q =…✨
  • pcungqr(3)generate an M-by-N complex distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal columns, which is defined as the first N columns of a product of K elementary reflectors of order M Q…✨
  • pcungr2(3)generate an M-by-N complex distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal rows, which is defined as the last M rows of a product of K elementary reflectors of order N Q = H(1)'…✨
  • pcungrq(3)generate an M-by-N complex distributed matrix Q denoting A(IA:IA+M-1,JA:JA+N-1) with orthonormal rows, which is defined as the last M rows of a product of K elementary reflectors of order N Q = H(1)'…✨
  • pcunm2l(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunm2r(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmbr(3)VECT = 'Q', PCUNMBR overwrites the general complex distributed M-by-N matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmhr(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunml2(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmlq(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmql(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmqr(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmr2(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmr3(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmrq(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmrz(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pcunmtr(3)overwrite the general complex M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1) with SIDE = 'L' SIDE = 'R' TRANS = 'N'✨
  • pddbsv(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pddbtrf(3)compute a LU factorization of an N-by-N real banded diagonally dominant-like distributed matrix with bandwidth BWL, BWU✨
  • pddbtrs(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pddbtrsv(3)solve a banded triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pddtsv(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pddttrf(3)compute a LU factorization of an N-by-N real tridiagonal diagonally dominant-like distributed matrix A(1:N, JA:JA+N-1)✨
  • pddttrs(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pddttrsv(3)solve a tridiagonal triangular system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pdgbsv(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pdgbtrf(3)compute a LU factorization of an N-by-N real banded distributed matrix with bandwidth BWL, BWU✨
  • pdgbtrs(3)solve a system of linear equations A(1:N, JA:JA+N-1) * X = B(IB:IB+N-1, 1:NRHS)✨
  • pdgebd2(3)reduce a real 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 orthogonal transformation✨
  • pdgebrd(3)reduce a real 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 orthogonal transformation✨
  • pdgecon(3)estimate the reciprocal of the condition number of a general distributed real 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 PDGETRF✨
  • pdgeequ(3)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✨
  • pdgehd2(3)reduce a real general distributed matrix sub( A ) to upper Hessenberg form H by an orthogonal similarity transforma- tion✨
  • pdgehrd(3)reduce a real general distributed matrix sub( A ) to upper Hessenberg form H by an orthogonal similarity transforma- tion✨
  • pdgelq2(3)compute a LQ factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = L * Q✨
  • pdgelqf(3)compute a LQ factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = L * Q✨
  • pdgels(3)solve overdetermined or underdetermined real linear systems involving an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1),✨
  • pdgeql2(3)compute a QL factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * L✨
  • pdgeqlf(3)compute a QL factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * L✨
  • pdgeqpf(3)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)✨
  • pdgeqr2(3)compute a QR factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * R✨
  • pdgeqrf(3)compute a QR factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = Q * R✨
  • pdgerfs(3)improve the computed solution to a system of linear equations and provides error bounds and backward error estimates for the solutions✨
  • pdgerq2(3)compute a RQ factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = R * Q✨
  • pdgerqf(3)compute a RQ factorization of a real distributed M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1) = R * Q✨
  • pdgesv(3)compute the solution to a real system of linear equations sub( A ) * X = sub( B ),✨
  • pdgesvd(3)compute the singular value decomposition (SVD) of an M-by-N matrix A, optionally computing the left and/or right singular vectors✨
  • pdgesvx(3)use the LU factorization to compute the solution to a real system of linear equations A(IA:IA+N-1,JA:JA+N-1) * X = B(IB:IB+N-1,JB:JB+NRHS-1),✨
  • pdgetf2(3)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✨
  • pdgetrf(3)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✨
  • pdgetri(3)compute the inverse of a distributed matrix using the LU factorization computed by PDGETRF✨
  • pdgetrs(3)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 PDGETRF✨
  • pdggqrf(3)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)✨
  • pdggrqf(3)compute a generalized RQ factorization of an M-by-N matrix sub( A ) = A(IA:IA+M-1,JA:JA+N-1)✨
  • pdlabad(3)take as input the values computed by PDLAMCH for underflow and overflow, and returns the square root of each of these values if the log of LARGE is sufficiently large✨
  • pdlabrd(3)reduce the first NB rows and columns of a real 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 orthogonal transformation Q' * A * P,✨
  • pdlacon(3)estimate the 1-norm of a square, real distributed matrix A✨
  • pdlaconsb(3)look for two consecutive small subdiagonal elements by seeing the effect of starting a double shift QR iteration given by H44, H33, & H43H34 and see if this would make a subdiagonal negligible✨
  • pdlacp2(3)copie all or part of a distributed matrix A to another distributed matrix B✨
  • pdlacp3(3)i an auxiliary routine that copies from a global parallel array into a local replicated array or vise versa✨
  • pdlacpy(3)copie all or part of a distributed matrix A to another distributed matrix B✨
  • pdlaevswp(3)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✨
  • pdlahqr(3)i an auxiliary routine used to find the Schur decomposition and or eigenvalues of a matrix already in Hessenberg form from cols ILO to IHI✨
  • pdlahrd(3)reduce the first NB columns of a real 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✨
  • pdlamch(3)determine double precision machine parameters✨
  • pdlange(3)return the value of the one norm, or the Frobenius norm,✨
  • pdlanhs(3)return the value of the one norm, or the Frobenius norm,✨
  • pdlansy(3)return the value of the one norm, or the Frobenius norm,✨
  • pdlantr(3)return the value of the one norm, or the Frobenius norm,✨
  • pdlapiv(3)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✨
  • pdlapv2(3)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✨
  • pdlaqge(3)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✨
  • pdlaqsy(3)equilibrate a symmetric distributed matrix sub( A ) = A(IA:IA+N-1,JA:JA+N-1) using the scaling factors in the vectors SR and SC✨
  • pdlared1d(3)redistribute a 1D array It assumes that the input array, BYCOL, is distributed across rows and that all process column contain the same copy of BYCOL✨
  • pdlared2d(3)redistribute a 1D array It assumes that the input array, BYROW, is distributed across columns and that all process rows contain the same copy of BYROW✨
  • pdlarf(3)applie a real elementary reflector Q (or Q**T) to a real M-by-N distributed matrix sub( C ) = C(IC:IC+M-1,JC:JC+N-1), from either the left or the right✨