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/* -*- mode: C -*- */
/*
IGraph library.
Copyright (C) 2010-2012 Gabor Csardi <csardi.gabor@gmail.com>
334 Harvard street, Cambridge, MA 02139 USA
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
02110-1301 USA
*/
#include "igraph_lapack.h"
#include "igraph_lapack_internal.h"
/**
* \function igraph_lapack_dgetrf
* LU factorization of a general M-by-N matrix
*
* The factorization has the form
* A = P * L * U
* where P is a permutation matrix, L is lower triangular with unit
* diagonal elements (lower trapezoidal if m > n), and U is upper
* triangular (upper trapezoidal if m < n).
* \param a The input/output matrix. On entry, the M-by-N matrix to be
* factored. On exit, the factors L and U from the factorization
* A = P * L * U; the unit diagonal elements of L are not
* stored.
* \param ipiv An integer vector, the pivot indices are stored here,
* unless it is a null pointer. Row i of the matrix was
* interchanged with row ipiv[i].
* \param info LAPACK error code. Zero on successful exit. If positive
* and i, then U(i,i) is exactly zero. The factorization has been
* completed, but the factor U is exactly singular, and division
* by zero will occur if it is used to solve a system of
* equations. If LAPACK returns an error, i.e. a negative info
* value, then an igraph error is generated as well.
* \return Error code.
*
* Time complexity: TODO.
*/
int igraph_lapack_dgetrf(igraph_matrix_t *a, igraph_vector_int_t *ipiv,
int *info) {
int m=(int) igraph_matrix_nrow(a);
int n=(int) igraph_matrix_ncol(a);
int lda=m > 0 ? m : 1;
igraph_vector_int_t *myipiv=ipiv, vipiv;
if (!ipiv) {
IGRAPH_CHECK(igraph_vector_int_init(&vipiv, m<n ? m : n));
IGRAPH_FINALLY(igraph_vector_int_destroy, &vipiv);
myipiv=&vipiv;
}
igraphdgetrf_(&m, &n, VECTOR(a->data), &lda, VECTOR(*myipiv), info);
if (*info > 0) {
IGRAPH_WARNING("LU: factor is exactly singular");
} else if (*info < 0) {
switch(*info) {
case -1:
IGRAPH_ERROR("Invalid number of rows", IGRAPH_ELAPACK);
break;
case -2:
IGRAPH_ERROR("Invalid number of columns", IGRAPH_ELAPACK);
break;
case -3:
IGRAPH_ERROR("Invalid input matrix", IGRAPH_ELAPACK);
break;
case -4:
IGRAPH_ERROR("Invalid LDA parameter", IGRAPH_ELAPACK);
break;
case -5:
IGRAPH_ERROR("Invalid pivot vector", IGRAPH_ELAPACK);
break;
case -6:
IGRAPH_ERROR("Invalid info argument", IGRAPH_ELAPACK);
break;
default:
IGRAPH_ERROR("Unknown LAPACK error", IGRAPH_ELAPACK);
break;
}
}
if (!ipiv) {
igraph_vector_int_destroy(&vipiv);
IGRAPH_FINALLY_CLEAN(1);
}
return 0;
}
/**
* \function igraph_lapack_dgetrs
* Solve general system of linear equations using LU factorization
*
* This function calls LAPACK to solve a system of linear equations
* A * X = B or A' * X = B
* with a general N-by-N matrix A using the LU factorization
* computed by \ref igraph_lapack_dgetrf.
* \param transpose Logical scalar, whether to transpose the input
* matrix.
* \param a A matrix containing the L and U factors from the
* factorization A = P*L*U.
* \param ipiv An integer vector, the pivot indices from \ref
* igraph_lapack_dgetrf must be given here.
* \param b The right hand side matrix must be given here.
* \return Error code.
*
* Time complexity: TODO.
*/
int igraph_lapack_dgetrs(igraph_bool_t transpose, const igraph_matrix_t *a,
igraph_vector_int_t *ipiv, igraph_matrix_t *b) {
char trans = transpose ? 'T' : 'N';
int n=(int) igraph_matrix_nrow(a);
int nrhs=(int) igraph_matrix_ncol(b);
int lda= n > 0 ? n : 1;
int ldb= n > 0 ? n : 1;
int info;
if (n != igraph_matrix_ncol(a)) {
IGRAPH_ERROR("Cannot LU solve matrix", IGRAPH_NONSQUARE);
}
if (n != igraph_matrix_nrow(b)) {
IGRAPH_ERROR("Cannot LU solve matrix, RHS of wrong size", IGRAPH_EINVAL);
}
igraphdgetrs_(&trans, &n, &nrhs, VECTOR(a->data), &lda, VECTOR(*ipiv),
VECTOR(b->data), &ldb, &info);
if (info < 0) {
switch(info) {
case -1:
IGRAPH_ERROR("Invalid transpose argument", IGRAPH_ELAPACK);
break;
case -2:
IGRAPH_ERROR("Invalid number of rows/columns", IGRAPH_ELAPACK);
break;
case -3:
IGRAPH_ERROR("Invalid number of RHS vectors", IGRAPH_ELAPACK);
break;
case -4:
IGRAPH_ERROR("Invalid LU matrix", IGRAPH_ELAPACK);
break;
case -5:
IGRAPH_ERROR("Invalid LDA parameter", IGRAPH_ELAPACK);
break;
case -6:
IGRAPH_ERROR("Invalid pivot vector", IGRAPH_ELAPACK);
break;
case -7:
IGRAPH_ERROR("Invalid RHS matrix", IGRAPH_ELAPACK);
break;
case -8:
IGRAPH_ERROR("Invalid LDB parameter", IGRAPH_ELAPACK);
break;
case -9:
IGRAPH_ERROR("Invalid info argument", IGRAPH_ELAPACK);
break;
default:
IGRAPH_ERROR("Unknown LAPACK error", IGRAPH_ELAPACK);
break;
}
}
return 0;
}
/**
* \function igraph_lapack_dgesv
* Solve system of linear equations with LU factorization
*
* This function computes the solution to a real system of linear
* equations A * X = B, where A is an N-by-N matrix and X and B are
* N-by-NRHS matrices.
*
* </para><para>The LU decomposition with partial pivoting and row
* interchanges is used to factor A as
* A = P * L * U,
* where P is a permutation matrix, L is unit lower triangular, and U is
* upper triangular. The factored form of A is then used to solve the
* system of equations A * X = B.
* \param a Matrix. On entry the N-by-N coefficient matrix, on exit,
* the factors L and U from the factorization A=P*L*U; the unit
* diagonal elements of L are not stored.
* \param ipiv An integer vector or a null pointer. If not a null
* pointer, then the pivot indices that define the permutation
* matrix P, are stored here. Row i of the matrix was
* interchanged with row IPIV(i).
* \param b Matrix, on entry the right hand side matrix should be
* stored here. On exit, if there was no error, and the info
* argument is zero, then it contains the solution matrix X.
* \param info The LAPACK info code. If it is positive, then
* U(info,info) is exactly zero. In this case the factorization
* has been completed, but the factor U is exactly
* singular, so the solution could not be computed.
* \return Error code.
*
* Time complexity: TODO.
*
* \example examples/simple/igraph_lapack_dgesv.c
*/
int igraph_lapack_dgesv(igraph_matrix_t *a, igraph_vector_int_t *ipiv,
igraph_matrix_t *b, int *info) {
int n=(int) igraph_matrix_nrow(a);
int nrhs=(int) igraph_matrix_ncol(b);
int lda= n > 0 ? n : 1;
int ldb= n > 0 ? n : 1;
igraph_vector_int_t *myipiv=ipiv, vipiv;
if (n != igraph_matrix_ncol(a)) {
IGRAPH_ERROR("Cannot LU solve matrix", IGRAPH_NONSQUARE);
}
if (n != igraph_matrix_nrow(b)) {
IGRAPH_ERROR("Cannot LU solve matrix, RHS of wrong size", IGRAPH_EINVAL);
}
if (!ipiv) {
IGRAPH_CHECK(igraph_vector_int_init(&vipiv, n));
IGRAPH_FINALLY(igraph_vector_int_destroy, &vipiv);
myipiv=&vipiv;
}
igraphdgesv_(&n, &nrhs, VECTOR(a->data), &lda, VECTOR(*myipiv),
VECTOR(b->data), &ldb, info);
if (*info > 0) {
IGRAPH_WARNING("LU: factor is exactly singular");
} else if (*info < 0) {
switch(*info) {
case -1:
IGRAPH_ERROR("Invalid number of rows/column", IGRAPH_ELAPACK);
break;
case -2:
IGRAPH_ERROR("Invalid number of RHS vectors", IGRAPH_ELAPACK);
break;
case -3:
IGRAPH_ERROR("Invalid input matrix", IGRAPH_ELAPACK);
break;
case -4:
IGRAPH_ERROR("Invalid LDA parameter", IGRAPH_ELAPACK);
break;
case -5:
IGRAPH_ERROR("Invalid pivot vector", IGRAPH_ELAPACK);
break;
case -6:
IGRAPH_ERROR("Invalid RHS matrix", IGRAPH_ELAPACK);
break;
case -7:
IGRAPH_ERROR("Invalid LDB parameter", IGRAPH_ELAPACK);
break;
case -8:
IGRAPH_ERROR("Invalid info argument", IGRAPH_ELAPACK);
break;
default:
IGRAPH_ERROR("Unknown LAPACK error", IGRAPH_ELAPACK);
break;
}
}
if (!ipiv) {
igraph_vector_int_destroy(&vipiv);
IGRAPH_FINALLY_CLEAN(1);
}
return 0;
}
/**
* \function igraph_lapack_dsyevr
* Selected eigenvalues and optionally eigenvectors of a symmetric matrix
*
* Calls the DSYEVR LAPACK function to compute selected eigenvalues
* and, optionally, eigenvectors of a real symmetric matrix A.
* Eigenvalues and eigenvectors can be selected by specifying either
* a range of values or a range of indices for the desired eigenvalues.
*
* </para><para>See more in the LAPACK documentation.
* \param A Matrix, on entry it contains the symmetric input
* matrix. Only the leading N-by-N upper triangular part is
* used for the computation.
* \param which Constant that gives which eigenvalues (and possibly
* the corresponding eigenvectors) to calculate. Possible
* values are \c IGRAPH_LAPACK_DSYEV_ALL, all eigenvalues;
* \c IGRAPH_LAPACK_DSYEV_INTERVAL, all eigenvalues in the
* half-open interval (vl,vu];
* \c IGRAPH_LAPACK_DSYEV_SELECT, the il-th through iu-th
* eigenvalues.
* \param vl If \p which is \c IGRAPH_LAPACK_DSYEV_INTERVAL, then
* this is the lower bound of the interval to be searched for
* eigenvalues. See also the \p vestimate argument.
* \param vu If \p which is \c IGRAPH_LAPACK_DSYEV_INTERVAL, then
* this is the upper bound of the interval to be searched for
* eigenvalues. See also the \p vestimate argument.
* \param vestimate An upper bound for the number of eigenvalues in
* the (vl,vu] interval, if \p which is \c
* IGRAPH_LAPACK_DSYEV_INTERVAL. Memory is allocated only for
* the given number of eigenvalues (and eigenvectors), so this
* upper bound must be correct.
* \param il The index of the smallest eigenvalue to return, if \p
* which is \c IGRAPH_LAPACK_DSYEV_SELECT.
* \param iu The index of the largets eigenvalue to return, if \p
* which is \c IGRAPH_LAPACK_DSYEV_SELECT.
* \param abstol The absolute error tolerance for the eigevalues. An
* approximate eigenvalue is accepted as converged when it is
* determined to lie in an interval [a,b] of width less than or
* equal to abstol + EPS * max(|a|,|b|), where EPS is the
* machine precision.
* \param values An initialized vector, the eigenvalues are stored
* here, unless it is a null pointer. It will be resized as
* needed.
* \param vectors An initialized matrix, the eigenvectors are stored
* in its columns, unless it is a null pointer. It will be
* resized as needed.
* \param support An integer vector. If not a null pointer, then it
* will be resized to (2*max(1,M)) (M is a the total number of
* eigenvalues found). Then the support of the eigenvectors in
* \p vectors is stored here, i.e., the indices
* indicating the nonzero elements in \p vectors.
* The i-th eigenvector is nonzero only in elements
* support(2*i-1) through support(2*i).
* \return Error code.
*
* Time complexity: TODO.
*
* \example examples/simple/igraph_lapack_dsyevr.c
*/
int igraph_lapack_dsyevr(const igraph_matrix_t *A,
igraph_lapack_dsyev_which_t which,
igraph_real_t vl, igraph_real_t vu, int vestimate,
int il, int iu, igraph_real_t abstol,
igraph_vector_t *values, igraph_matrix_t *vectors,
igraph_vector_int_t *support) {
igraph_matrix_t Acopy;
char jobz = vectors ? 'V' : 'N', range, uplo='U';
int n=(int) igraph_matrix_nrow(A), lda=n, ldz=n;
int m, info;
igraph_vector_t *myvalues=values, vvalues;
igraph_vector_int_t *mysupport=support, vsupport;
igraph_vector_t work;
igraph_vector_int_t iwork;
int lwork=-1, liwork=-1;
if (n != igraph_matrix_ncol(A)) {
IGRAPH_ERROR("Cannot find eigenvalues/vectors", IGRAPH_NONSQUARE);
}
if (which==IGRAPH_LAPACK_DSYEV_INTERVAL &&
(vestimate < 1 || vestimate > n)) {
IGRAPH_ERROR("Estimated (upper bound) number of eigenvalues must be "
"between 1 and n", IGRAPH_EINVAL);
}
if (which==IGRAPH_LAPACK_DSYEV_SELECT && iu-il < 0) {
IGRAPH_ERROR("Invalid 'il' and/or 'iu' values", IGRAPH_EINVAL);
}
IGRAPH_CHECK(igraph_matrix_copy(&Acopy, A));
IGRAPH_FINALLY(igraph_matrix_destroy, &Acopy);
IGRAPH_VECTOR_INIT_FINALLY(&work, 1);
IGRAPH_CHECK(igraph_vector_int_init(&iwork, 1));
IGRAPH_FINALLY(igraph_vector_int_destroy, &iwork);
if (!values) {
IGRAPH_VECTOR_INIT_FINALLY(&vvalues, 0);
myvalues=&vvalues;
}
if (!support) {
IGRAPH_CHECK(igraph_vector_int_init(&vsupport, 0));
IGRAPH_FINALLY(igraph_vector_int_destroy, &vsupport);
mysupport=&vsupport;
}
switch (which) {
case IGRAPH_LAPACK_DSYEV_ALL:
range = 'A';
IGRAPH_CHECK(igraph_vector_resize(myvalues, n));
IGRAPH_CHECK(igraph_vector_int_resize(mysupport, 2*n));
if (vectors) { IGRAPH_CHECK(igraph_matrix_resize(vectors, n, n)); }
break;
case IGRAPH_LAPACK_DSYEV_INTERVAL:
range = 'V';
IGRAPH_CHECK(igraph_vector_resize(myvalues, vestimate));
IGRAPH_CHECK(igraph_vector_int_resize(mysupport, 2*vestimate));
if (vectors) { IGRAPH_CHECK(igraph_matrix_resize(vectors,n, vestimate)); }
break;
case IGRAPH_LAPACK_DSYEV_SELECT:
range = 'I';
IGRAPH_CHECK(igraph_vector_resize(myvalues, iu-il+1));
IGRAPH_CHECK(igraph_vector_int_resize(mysupport, 2*(iu-il+1)));
if (vectors) { IGRAPH_CHECK(igraph_matrix_resize(vectors, n, iu-il+1)); }
break;
}
igraphdsyevr_(&jobz, &range, &uplo, &n, &MATRIX(Acopy,0,0), &lda,
&vl, &vu, &il, &iu, &abstol, &m, VECTOR(*myvalues),
vectors ? &MATRIX(*vectors,0,0) : 0, &ldz, VECTOR(*mysupport),
VECTOR(work), &lwork, VECTOR(iwork), &liwork, &info);
lwork=(int) VECTOR(work)[0];
liwork=VECTOR(iwork)[0];
IGRAPH_CHECK(igraph_vector_resize(&work, lwork));
IGRAPH_CHECK(igraph_vector_int_resize(&iwork, liwork));
igraphdsyevr_(&jobz, &range, &uplo, &n, &MATRIX(Acopy,0,0), &lda,
&vl, &vu, &il, &iu, &abstol, &m, VECTOR(*myvalues),
vectors ? &MATRIX(*vectors,0,0) : 0, &ldz, VECTOR(*mysupport),
VECTOR(work), &lwork, VECTOR(iwork), &liwork, &info);
if (values) {
IGRAPH_CHECK(igraph_vector_resize(values, m));
}
if (vectors) {
IGRAPH_CHECK(igraph_matrix_resize(vectors, n, m));
}
if (support) {
IGRAPH_CHECK(igraph_vector_int_resize(support, m));
}
if (!support) {
igraph_vector_int_destroy(&vsupport);
IGRAPH_FINALLY_CLEAN(1);
}
if (!values) {
igraph_vector_destroy(&vvalues);
IGRAPH_FINALLY_CLEAN(1);
}
igraph_vector_int_destroy(&iwork);
igraph_vector_destroy(&work);
igraph_matrix_destroy(&Acopy);
IGRAPH_FINALLY_CLEAN(3);
return 0;
}
/**
* \function igraph_lapack_dgeev
* Eigenvalues and optionally eigenvectors of a non-symmetric matrix
*
* This function calls LAPACK to compute, for an N-by-N real
* nonsymmetric matrix A, the eigenvalues and, optionally, the left
* and/or right eigenvectors.
*
* </para><para>
* The right eigenvector v(j) of A satisfies
* A * v(j) = lambda(j) * v(j)
* where lambda(j) is its eigenvalue.
* The left eigenvector u(j) of A satisfies
* u(j)**H * A = lambda(j) * u(j)**H
* where u(j)**H denotes the conjugate transpose of u(j).
*
* </para><para>
* The computed eigenvectors are normalized to have Euclidean norm
* equal to 1 and largest component real.
*
* \param A matrix. On entry it contains the N-by-N input matrix.
* \param valuesreal Pointer to an initialized vector, or a null
* pointer. If not a null pointer, then the real parts of the
* eigenvalues are stored here. The vector will be resized as
* needed.
* \param valuesimag Pointer to an initialized vector, or a null
* pointer. If not a null pointer, then the imaginary parts of
* the eigenvalues are stored here. The vector will be resized
* as needed.
* \param vectorsleft Pointer to an initialized matrix, or a null
* pointer. If not a null pointer, then the left eigenvectors
* are stored in the columns of the matrix. The matrix will be
* resized as needed.
* \param vectorsright Pointer to an initialized matrix, or a null
* pointer. If not a null pointer, then the right eigenvectors
* are stored in the columns of the matrix. The matrix will be
* resized as needed.
* \param info This argument is used for two purposes. As an input
* argument it gives whether an igraph error should be
* generated if the QR algorithm fails to compute all
* eigenvalues. If \p info is non-zero, then an error is
* generated, otherwise only a warning is given.
* On exit it contains the LAPACK error code.
* Zero means successful exit.
* A negative values means that some of the arguments had an
* illegal value, this always triggers an igraph error. An i
* positive value means that the QR algorithm failed to
* compute all the eigenvalues, and no eigenvectors have been
* computed; element i+1:N of \p valuesreal and \p valuesimag
* contain eigenvalues which have converged. This case only
* generates an igraph error, if \p info was non-zero on entry.
* \return Error code.
*
* Time complexity: TODO.
*
* \example examples/simple/igraph_lapack_dgeev.c
*/
int igraph_lapack_dgeev(const igraph_matrix_t *A,
igraph_vector_t *valuesreal,
igraph_vector_t *valuesimag,
igraph_matrix_t *vectorsleft,
igraph_matrix_t *vectorsright,
int *info) {
char jobvl= vectorsleft ? 'V' : 'N';
char jobvr= vectorsright ? 'V' : 'N';
int n=(int) igraph_matrix_nrow(A);
int lda=n, ldvl=n, ldvr=n, lwork=-1;
igraph_vector_t work;
igraph_vector_t *myreal=valuesreal, *myimag=valuesimag, vreal, vimag;
igraph_matrix_t Acopy;
int error=*info;
if (igraph_matrix_ncol(A) != n) {
IGRAPH_ERROR("Cannot calculate eigenvalues (dgeev)", IGRAPH_NONSQUARE);
}
IGRAPH_CHECK(igraph_matrix_copy(&Acopy, A));
IGRAPH_FINALLY(igraph_matrix_destroy, &Acopy);
IGRAPH_VECTOR_INIT_FINALLY(&work, 1);
if (!valuesreal) {
IGRAPH_VECTOR_INIT_FINALLY(&vreal, n);
myreal=&vreal;
} else {
IGRAPH_CHECK(igraph_vector_resize(myreal, n));
}
if (!valuesimag) {
IGRAPH_VECTOR_INIT_FINALLY(&vimag, n);
myimag=&vimag;
} else {
IGRAPH_CHECK(igraph_vector_resize(myimag, n));
}
if (vectorsleft) {
IGRAPH_CHECK(igraph_matrix_resize(vectorsleft, n, n));
}
if (vectorsright) {
IGRAPH_CHECK(igraph_matrix_resize(vectorsright, n, n));
}
igraphdgeev_(&jobvl, &jobvr, &n, &MATRIX(Acopy,0,0), &lda,
VECTOR(*myreal), VECTOR(*myimag),
vectorsleft ? &MATRIX(*vectorsleft ,0,0) : 0, &ldvl,
vectorsright ? &MATRIX(*vectorsright,0,0) : 0, &ldvr,
VECTOR(work), &lwork, info);
lwork=(int) VECTOR(work)[0];
IGRAPH_CHECK(igraph_vector_resize(&work, lwork));
igraphdgeev_(&jobvl, &jobvr, &n, &MATRIX(Acopy,0,0), &lda,
VECTOR(*myreal), VECTOR(*myimag),
vectorsleft ? &MATRIX(*vectorsleft ,0,0) : 0, &ldvl,
vectorsright ? &MATRIX(*vectorsright,0,0) : 0, &ldvr,
VECTOR(work), &lwork, info);
if (*info < 0) {
IGRAPH_ERROR("Cannot calculate eigenvalues (dgeev)", IGRAPH_ELAPACK);
} else if (*info > 0) {
if (error) {
IGRAPH_ERROR("Cannot calculate eigenvalues (dgeev)", IGRAPH_ELAPACK);
} else {
IGRAPH_WARNING("Cannot calculate eigenvalues (dgeev)");
}
}
if (!valuesimag) {
igraph_vector_destroy(&vimag);
IGRAPH_FINALLY_CLEAN(1);
}
if (!valuesreal) {
igraph_vector_destroy(&vreal);
IGRAPH_FINALLY_CLEAN(1);
}
igraph_vector_destroy(&work);
igraph_matrix_destroy(&Acopy);
IGRAPH_FINALLY_CLEAN(2);
return 0;
}
/**
* \function igraph_lapack_dgeevx
* Eigenvalues/vectors of nonsymmetric matrices, expert mode
*
* This function calculates the eigenvalues and optionally the left
* and/or right eigenvectors of a nonsymmetric N-by-N real matrix.
*
* </para><para>
* Optionally also, it computes a balancing transformation to improve
* the conditioning of the eigenvalues and eigenvectors (\p ilo, \pihi,
* \p scale, and \p abnrm), reciprocal condition numbers for the
* eigenvalues (\p rconde), and reciprocal condition numbers for the
* right eigenvectors (\p rcondv).
*
* </para><para>
* The right eigenvector v(j) of A satisfies
* A * v(j) = lambda(j) * v(j)
* where lambda(j) is its eigenvalue.
* The left eigenvector u(j) of A satisfies
* u(j)**H * A = lambda(j) * u(j)**H
* where u(j)**H denotes the conjugate transpose of u(j).
*
* </para><para>
* The computed eigenvectors are normalized to have Euclidean norm
* equal to 1 and largest component real.
*
* </para><para>
* Balancing a matrix means permuting the rows and columns to make it
* more nearly upper triangular, and applying a diagonal similarity
* transformation D * A * D**(-1), where D is a diagonal matrix, to
* make its rows and columns closer in norm and the condition numbers
* of its eigenvalues and eigenvectors smaller. The computed
* reciprocal condition numbers correspond to the balanced matrix.
* Permuting rows and columns will not change the condition numbers
* (in exact arithmetic) but diagonal scaling will. For further
* explanation of balancing, see section 4.10.2 of the LAPACK
* Users' Guide.
*
* \param balance Scalar that indicated, whether the input matrix
* should be balanced. Possible values:
* \clist
* \cli IGRAPH_LAPACK_DGEEVX_BALANCE_NONE
* no not diagonally scale or permute.
* \cli IGRAPH_LAPACK_DGEEVX_BALANCE_PERM
* perform permutations to make the matrix more nearly upper
* triangular. Do not diagonally scale.
* \cli IGRAPH_LAPACK_DGEEVX_BALANCE_SCALE
* diagonally scale the matrix, i.e. replace A by
* D*A*D**(-1), where D is a diagonal matrix, chosen to make
* the rows and columns of A more equal in norm. Do not
* permute.
* \cli IGRAPH_LAPACK_DGEEVX_BALANCE_BOTH
* both diagonally scale and permute A.
* \endclist
* \param A The input matrix, must be square.
* \param valuesreal An initialized vector, or a NULL pointer. If not
* a NULL pointer, then the real parts of the eigenvalues are stored
* here. The vector will be resized, as needed.
* \param valuesimag An initialized vector, or a NULL pointer. If not
* a NULL pointer, then the imaginary parts of the eigenvalues are stored
* here. The vector will be resized, as needed.
* \param vectorsleft An initialized matrix or a NULL pointer. If not
* a null pointer, then the left eigenvectors are stored here. The
* order corresponds to the eigenvalues and the eigenvectors are
* stored in a compressed form. If the j-th eigenvalue is real then
* column j contains the corresponding eigenvector. If the j-th and
* (j+1)-th eigenvalues form a complex conjugate pair, then the j-th
* and (j+1)-th columns contain their corresponding eigenvectors.
* \param vectorsright An initialized matrix or a NULL pointer. If not
* a null pointer, then the right eigenvectors are stored here. The
* format is the same, as for the \p vectorsleft argument.
* \param ilo
* \param ihi \p ilo and \p ihi are integer values determined when A was
* balanced. The balanced A(i,j) = 0 if I>J and
* J=1,...,ilo-1 or I=ihi+1,...,N.
* \param scale Pointer to an initialized vector or a NULL pointer. If
* not a NULL pointer, then details of the permutations and scaling
* factors applied when balancing \param A, are stored here.
* If P(j) is the index of the row and column
* interchanged with row and column j, and D(j) is the scaling
* factor applied to row and column j, then
* \clist
* \cli scale(J) = P(J), for J = 1,...,ilo-1
* \cli scale(J) = D(J), for J = ilo,...,ihi
* \cli scale(J) = P(J) for J = ihi+1,...,N.
* \endclist
* The order in which the interchanges are made is N to \p ihi+1,
* then 1 to \p ilo-1.
* \param abnrm Pointer to a real variable, the one-norm of the
* balanced matrix is stored here. (The one-norm is the maximum of
* the sum of absolute values of elements in any column.)
* \param rconde An initialized vector or a NULL pointer. If not a
* null pointer, then the reciprocal condition numbers of the
* eigenvalues are stored here.
* \param rcondv An initialized vector or a NULL pointer. If not a
* null pointer, then the reciprocal condition numbers of the right
* eigenvectors are stored here.
* \param info This argument is used for two purposes. As an input
* argument it gives whether an igraph error should be
* generated if the QR algorithm fails to compute all
* eigenvalues. If \p info is non-zero, then an error is
* generated, otherwise only a warning is given.
* On exit it contains the LAPACK error code.
* Zero means successful exit.
* A negative values means that some of the arguments had an
* illegal value, this always triggers an igraph error. An i
* positive value means that the QR algorithm failed to
* compute all the eigenvalues, and no eigenvectors have been
* computed; element i+1:N of \p valuesreal and \p valuesimag
* contain eigenvalues which have converged. This case only
* generated an igraph error, if \p info was non-zero on entry.
* \return Error code.
*
* Time complexity: TODO
*
* \example examples/simple/igraph_lapack_dgeevx.c
*/
int igraph_lapack_dgeevx(igraph_lapack_dgeevx_balance_t balance,
const igraph_matrix_t *A,
igraph_vector_t *valuesreal,
igraph_vector_t *valuesimag,
igraph_matrix_t *vectorsleft,
igraph_matrix_t *vectorsright,
int *ilo, int *ihi, igraph_vector_t *scale,
igraph_real_t *abnrm,
igraph_vector_t *rconde,
igraph_vector_t *rcondv,
int *info) {
char balanc;
char jobvl= vectorsleft ? 'V' : 'N';
char jobvr= vectorsright ? 'V' : 'N';
char sense;
int n=(int) igraph_matrix_nrow(A);
int lda=n, ldvl=n, ldvr=n, lwork=-1;
igraph_vector_t work;
igraph_vector_int_t iwork;
igraph_matrix_t Acopy;
int error=*info;
igraph_vector_t *myreal=valuesreal, *myimag=valuesimag, vreal, vimag;
igraph_vector_t *myscale=scale, vscale;
if (igraph_matrix_ncol(A) != n) {
IGRAPH_ERROR("Cannot calculate eigenvalues (dgeevx)", IGRAPH_NONSQUARE);
}
switch (balance) {
case IGRAPH_LAPACK_DGEEVX_BALANCE_NONE:
balanc='N';
break;
case IGRAPH_LAPACK_DGEEVX_BALANCE_PERM:
balanc='P';
break;
case IGRAPH_LAPACK_DGEEVX_BALANCE_SCALE:
balanc='S';
break;
case IGRAPH_LAPACK_DGEEVX_BALANCE_BOTH:
balanc='B';
break;
default:
IGRAPH_ERROR("Invalid 'balance' argument", IGRAPH_EINVAL);
break;
}
if (!rconde && !rcondv) {
sense='N';
} else if (rconde && !rcondv) {
sense='E';
} else if (!rconde && rcondv) {
sense='V';
} else {
sense='B';
}
IGRAPH_CHECK(igraph_matrix_copy(&Acopy, A));
IGRAPH_FINALLY(igraph_matrix_destroy, &Acopy);
IGRAPH_VECTOR_INIT_FINALLY(&work, 1);
IGRAPH_CHECK(igraph_vector_int_init(&iwork, n));
IGRAPH_FINALLY(igraph_vector_int_destroy, &iwork);
if (!valuesreal) {
IGRAPH_VECTOR_INIT_FINALLY(&vreal, n);
myreal=&vreal;
} else {
IGRAPH_CHECK(igraph_vector_resize(myreal, n));
}
if (!valuesimag) {
IGRAPH_VECTOR_INIT_FINALLY(&vimag, n);
myimag=&vimag;
} else {
IGRAPH_CHECK(igraph_vector_resize(myimag, n));
}
if (!scale) {
IGRAPH_VECTOR_INIT_FINALLY(&vscale, n);
myscale=&vscale;
} else {
IGRAPH_CHECK(igraph_vector_resize(scale, n));
}
if (vectorsleft) {
IGRAPH_CHECK(igraph_matrix_resize(vectorsleft, n, n));
}
if (vectorsright) {
IGRAPH_CHECK(igraph_matrix_resize(vectorsright, n, n));
}
igraphdgeevx_(&balanc, &jobvl, &jobvr, &sense, &n, &MATRIX(Acopy,0,0),
&lda, VECTOR(*myreal), VECTOR(*myimag),
vectorsleft ? &MATRIX(*vectorsleft ,0,0) : 0, &ldvl,
vectorsright ? &MATRIX(*vectorsright,0,0) : 0, &ldvr,
ilo, ihi, VECTOR(*myscale), abnrm,
rconde ? VECTOR(*rconde) : 0,
rcondv ? VECTOR(*rcondv) : 0,
VECTOR(work), &lwork, VECTOR(iwork), info);
lwork=(int) VECTOR(work)[0];
IGRAPH_CHECK(igraph_vector_resize(&work, lwork));
igraphdgeevx_(&balanc, &jobvl, &jobvr, &sense, &n, &MATRIX(Acopy,0,0),
&lda, VECTOR(*myreal), VECTOR(*myimag),
vectorsleft ? &MATRIX(*vectorsleft ,0,0) : 0, &ldvl,
vectorsright ? &MATRIX(*vectorsright,0,0) : 0, &ldvr,
ilo, ihi, VECTOR(*myscale), abnrm,
rconde ? VECTOR(*rconde) : 0,
rcondv ? VECTOR(*rcondv) : 0,
VECTOR(work), &lwork, VECTOR(iwork), info);
if (*info < 0) {
IGRAPH_ERROR("Cannot calculate eigenvalues (dgeev)", IGRAPH_ELAPACK);
} else if (*info > 0) {
if (error) {
IGRAPH_ERROR("Cannot calculate eigenvalues (dgeev)", IGRAPH_ELAPACK);
} else {
IGRAPH_WARNING("Cannot calculate eigenvalues (dgeev)");
}
}
if (!scale) {
igraph_vector_destroy(&vscale);
IGRAPH_FINALLY_CLEAN(1);
}
if (!valuesimag) {
igraph_vector_destroy(&vimag);
IGRAPH_FINALLY_CLEAN(1);
}
if (!valuesreal) {
igraph_vector_destroy(&vreal);
IGRAPH_FINALLY_CLEAN(1);
}
igraph_vector_int_destroy(&iwork);
igraph_vector_destroy(&work);
igraph_matrix_destroy(&Acopy);
IGRAPH_FINALLY_CLEAN(3);
return 0;
}
int igraph_lapack_dgehrd(const igraph_matrix_t *A,
int ilo, int ihi,
igraph_matrix_t *result) {
int n=(int) igraph_matrix_nrow(A);
int lda=n;
int lwork=-1;
igraph_vector_t work;
igraph_real_t optwork;
igraph_vector_t tau;
igraph_matrix_t Acopy;
int info=0;
int i;
if (igraph_matrix_ncol(A) != n) {
IGRAPH_ERROR("Hessenberg reduction failed", IGRAPH_NONSQUARE);
}
if (ilo < 1 || ihi > n || ilo > ihi) {
IGRAPH_ERROR("Invalid `ilo' and/or `ihi'", IGRAPH_EINVAL);
}
if (n <= 1) {
IGRAPH_CHECK(igraph_matrix_update(result, A));
return 0;
}
IGRAPH_CHECK(igraph_matrix_copy(&Acopy, A));
IGRAPH_FINALLY(igraph_matrix_destroy, &Acopy);
IGRAPH_VECTOR_INIT_FINALLY(&tau, n-1);
igraphdgehrd_(&n, &ilo, &ihi, &MATRIX(Acopy, 0, 0), &lda, VECTOR(tau),
&optwork, &lwork, &info);
if (info != 0) {
IGRAPH_ERROR("Internal Hessenberg transformation error",
IGRAPH_EINTERNAL);
}
lwork=(int) optwork;
IGRAPH_VECTOR_INIT_FINALLY(&work, lwork);
igraphdgehrd_(&n, &ilo, &ihi, &MATRIX(Acopy, 0, 0), &lda, VECTOR(tau),
VECTOR(work), &lwork, &info);
if (info != 0) {
IGRAPH_ERROR("Internal Hessenberg transformation error",
IGRAPH_EINTERNAL);
}
igraph_vector_destroy(&work);
igraph_vector_destroy(&tau);
IGRAPH_FINALLY_CLEAN(2);
IGRAPH_CHECK(igraph_matrix_update(result, &Acopy));
igraph_matrix_destroy(&Acopy);
IGRAPH_FINALLY_CLEAN(1);
for (i=0; i<n-2; i++) {
int j;
for (j=i+2; j<n; j++) {
MATRIX(*result, j, i) = 0.0;
}
}
return 0;
}
int igraph_lapack_ddot(const igraph_vector_t *v1, const igraph_vector_t *v2,
igraph_real_t *res) {
int n=igraph_vector_size(v1);
int one=1;
if (igraph_vector_size(v2) != n) {
IGRAPH_ERROR("Dot product of vectors with different dimensions",
IGRAPH_EINVAL);
}
*res = igraphddot_(&n, VECTOR(*v1), &one, VECTOR(*v2), &one);
return 0;
}
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