org.netlib.lapack
Class Dsyevr
java.lang.Object
org.netlib.lapack.Dsyevr
public class Dsyevr
- extends java.lang.Object
Following is the description from the original
Fortran source. For each array argument, the Java
version will include an integer offset parameter, so
the arguments may not match the description exactly.
Contact seymour@cs.utk.edu with any questions.
* ..
*
* Purpose
* =======
*
* DSYEVR computes 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.
*
* DSYEVR first reduces the matrix A to tridiagonal form T with a call
* to DSYTRD. Then, whenever possible, DSYEVR calls DSTEMR to compute
* the eigenspectrum using Relatively Robust Representations. DSTEMR
* computes eigenvalues by the dqds algorithm, while orthogonal
* eigenvectors are computed from various "good" L D L^T representations
* (also known as Relatively Robust Representations). Gram-Schmidt
* orthogonalization is avoided as far as possible. More specifically,
* the various steps of the algorithm are as follows.
*
* For each unreduced block (submatrix) of T,
* (a) Compute T - sigma I = L D L^T, so that L and D
* define all the wanted eigenvalues to high relative accuracy.
* This means that small relative changes in the entries of D and
* cause only small relative changes in the eigenvalues and
* eigenvectors. The standard (unfactored) representation of the
* tridiagonal matrix T does not have this property in general.
* (b) Compute the eigenvalues to suitable accuracy.
* If the eigenvectors are desired, the algorithm attains full
* accuracy of the computed eigenvalues only right before
* the corresponding vectors have to be computed, see steps c) an
* (c) For each cluster of close eigenvalues, select a new
* shift close to the cluster, find a new factorization, and refi
* the shifted eigenvalues to suitable accuracy.
* (d) For each eigenvalue with a large enough relative separation co
* the corresponding eigenvector by forming a rank revealing twis
* factorization. Go back to (c) for any clusters that remain.
*
* The desired accuracy of the output can be specified by the input
* parameter ABSTOL.
*
* For more details, see DSTEMR's documentation and:
* - Inderjit S. Dhillon and Beresford N. Parlett: "Multiple representat
* to compute orthogonal eigenvectors of symmetric tridiagonal matrice
* Linear Algebra and its Applications, 387(1), pp. 1-28, August 2004.
* - Inderjit Dhillon and Beresford Parlett: "Orthogonal Eigenvectors an
* Relative Gaps," SIAM Journal on Matrix Analysis and Applications, V
* 2004. Also LAPACK Working Note 154.
* - Inderjit Dhillon: "A new O(n^2) algorithm for the symmetric
* tridiagonal eigenvalue/eigenvector problem",
* Computer Science Division Technical Report No. UCB/CSD-97-971,
* UC Berkeley, May 1997.
*
*
* Note 1 : DSYEVR calls DSTEMR when the full spectrum is requested
* on machines which conform to the ieee-754 floating point standard.
* DSYEVR calls DSTEBZ and SSTEIN on non-ieee machines and
* when partial spectrum requests are made.
*
* Normal execution of DSTEMR may create NaNs and infinities and
* hence may abort due to a floating point exception in environments
* which do not handle NaNs and infinities in the ieee standard default
* manner.
*
* Arguments
* =========
*
* JOBZ (input) CHARACTER*1
* = 'N': Compute eigenvalues only;
* = 'V': Compute eigenvalues and eigenvectors.
*
* RANGE (input) CHARACTER*1
* = 'A': all eigenvalues will be found.
* = 'V': all eigenvalues in the half-open interval (VL,VU]
* will be found.
* = 'I': the IL-th through IU-th eigenvalues will be found.
********** For RANGE = 'V' or 'I' and IU - IL < N - 1, DSTEBZ and
********** DSTEIN are called
*
* UPLO (input) CHARACTER*1
* = 'U': Upper triangle of A is stored;
* = 'L': Lower triangle of A is stored.
*
* N (input) INTEGER
* The order of the matrix A. N >= 0.
*
* A (input/output) DOUBLE PRECISION array, dimension (LDA, N)
* On entry, the symmetric matrix A. If UPLO = 'U', the
* leading N-by-N upper triangular part of A contains the
* upper triangular part of the matrix A. If UPLO = 'L',
* the leading N-by-N lower triangular part of A contains
* the lower triangular part of the matrix A.
* On exit, the lower triangle (if UPLO='L') or the upper
* triangle (if UPLO='U') of A, including the diagonal, is
* destroyed.
*
* LDA (input) INTEGER
* The leading dimension of the array A. LDA >= max(1,N).
*
* VL (input) DOUBLE PRECISION
* VU (input) DOUBLE PRECISION
* If RANGE='V', the lower and upper bounds of the interval to
* be searched for eigenvalues. VL < VU.
* Not referenced if RANGE = 'A' or 'I'.
*
* IL (input) INTEGER
* IU (input) INTEGER
* If RANGE='I', the indices (in ascending order) of the
* smallest and largest eigenvalues to be returned.
* 1 <= IL <= IU <= N, if N > 0; IL = 1 and IU = 0 if N = 0.
* Not referenced if RANGE = 'A' or 'V'.
*
* ABSTOL (input) DOUBLE PRECISION
* The absolute error tolerance for the eigenvalues.
* 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. If ABSTOL is less than
* or equal to zero, then EPS*|T| will be used in its place,
* where |T| is the 1-norm of the tridiagonal matrix obtained
* by reducing A to tridiagonal form.
*
* See "Computing Small Singular Values of Bidiagonal Matrices
* with Guaranteed High Relative Accuracy," by Demmel and
* Kahan, LAPACK Working Note #3.
*
* If high relative accuracy is important, set ABSTOL to
* DLAMCH( 'Safe minimum' ). Doing so will guarantee that
* eigenvalues are computed to high relative accuracy when
* possible in future releases. The current code does not
* make any guarantees about high relative accuracy, but
* future releases will. See J. Barlow and J. Demmel,
* "Computing Accurate Eigensystems of Scaled Diagonally
* Dominant Matrices", LAPACK Working Note #7, for a discussion
* of which matrices define their eigenvalues to high relative
* accuracy.
*
* M (output) INTEGER
* The total number of eigenvalues found. 0 <= M <= N.
* If RANGE = 'A', M = N, and if RANGE = 'I', M = IU-IL+1.
*
* W (output) DOUBLE PRECISION array, dimension (N)
* The first M elements contain the selected eigenvalues in
* ascending order.
*
* Z (output) DOUBLE PRECISION array, dimension (LDZ, max(1,M))
* If JOBZ = 'V', then if INFO = 0, the first M columns of Z
* contain the orthonormal eigenvectors of the matrix A
* corresponding to the selected eigenvalues, with the i-th
* column of Z holding the eigenvector associated with W(i).
* If JOBZ = 'N', then Z is not referenced.
* Note: the user must ensure that at least max(1,M) columns are
* supplied in the array Z; if RANGE = 'V', the exact value of M
* is not known in advance and an upper bound must be used.
* Supplying N columns is always safe.
*
* LDZ (input) INTEGER
* The leading dimension of the array Z. LDZ >= 1, and if
* JOBZ = 'V', LDZ >= max(1,N).
*
* ISUPPZ (output) INTEGER array, dimension ( 2*max(1,M) )
* The support of the eigenvectors in Z, i.e., the indices
* indicating the nonzero elements in Z. The i-th eigenvector
* is nonzero only in elements ISUPPZ( 2*i-1 ) through
* ISUPPZ( 2*i ).
********** Implemented only for RANGE = 'A' or 'I' and IU - IL = N - 1
*
* WORK (workspace/output) DOUBLE PRECISION array, dimension (MAX(1,L
* On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
*
* LWORK (input) INTEGER
* The dimension of the array WORK. LWORK >= max(1,26*N).
* For optimal efficiency, LWORK >= (NB+6)*N,
* where NB is the max of the blocksize for DSYTRD and DORMTR
* returned by ILAENV.
*
* If LWORK = -1, then a workspace query is assumed; the routine
* only calculates the optimal size of the WORK array, returns
* this value as the first entry of the WORK array, and no error
* message related to LWORK is issued by XERBLA.
*
* IWORK (workspace/output) INTEGER array, dimension (MAX(1,LIWORK))
* On exit, if INFO = 0, IWORK(1) returns the optimal LWORK.
*
* LIWORK (input) INTEGER
* The dimension of the array IWORK. LIWORK >= max(1,10*N).
*
* If LIWORK = -1, then a workspace query is assumed; the
* routine only calculates the optimal size of the IWORK array,
* returns this value as the first entry of the IWORK array, and
* no error message related to LIWORK is issued by XERBLA.
*
* INFO (output) INTEGER
* = 0: successful exit
* < 0: if INFO = -i, the i-th argument had an illegal value
* > 0: Internal error
*
* Further Details
* ===============
*
* Based on contributions by
* Inderjit Dhillon, IBM Almaden, USA
* Osni Marques, LBNL/NERSC, USA
* Ken Stanley, Computer Science Division, University of
* California at Berkeley, USA
* Jason Riedy, Computer Science Division, University of
* California at Berkeley, USA
*
* =====================================================================
*
* .. Parameters ..
Method Summary |
static void |
dsyevr(java.lang.String jobz,
java.lang.String range,
java.lang.String uplo,
int n,
double[] a,
int _a_offset,
int lda,
double vl,
double vu,
int il,
int iu,
double abstol,
intW m,
double[] w,
int _w_offset,
double[] z,
int _z_offset,
int ldz,
int[] isuppz,
int _isuppz_offset,
double[] work,
int _work_offset,
int lwork,
int[] iwork,
int _iwork_offset,
int liwork,
intW info)
|
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Dsyevr
public Dsyevr()
dsyevr
public static void dsyevr(java.lang.String jobz,
java.lang.String range,
java.lang.String uplo,
int n,
double[] a,
int _a_offset,
int lda,
double vl,
double vu,
int il,
int iu,
double abstol,
intW m,
double[] w,
int _w_offset,
double[] z,
int _z_offset,
int ldz,
int[] isuppz,
int _isuppz_offset,
double[] work,
int _work_offset,
int lwork,
int[] iwork,
int _iwork_offset,
int liwork,
intW info)