nag_dspgvd (f08tcc) (PDF version)
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NAG Library Manual

NAG Library Function Document

nag_dspgvd (f08tcc)

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_dspgvd (f08tcc) computes all the eigenvalues and, optionally, the eigenvectors of a real generalized symmetric-definite eigenproblem, of the form
Az=λBz ,   ABz=λz   or   BAz=λz ,
where A and B are symmetric, stored in packed format, and B is also positive definite. If eigenvectors are desired, it uses a divide-and-conquer algorithm.

2  Specification

#include <nag.h>
#include <nagf08.h>
void  nag_dspgvd (Nag_OrderType order, Integer itype, Nag_JobType job, Nag_UploType uplo, Integer n, double ap[], double bp[], double w[], double z[], Integer pdz, NagError *fail)

3  Description

nag_dspgvd (f08tcc) first performs a Cholesky factorization of the matrix B as B=UTU , when uplo=Nag_Upper or B=LLT , when uplo=Nag_Lower. The generalized problem is then reduced to a standard symmetric eigenvalue problem
Cx=λx ,
which is solved for the eigenvalues and, optionally, the eigenvectors; the eigenvectors are then backtransformed to give the eigenvectors of the original problem.
For the problem Az=λBz , the eigenvectors are normalized so that the matrix of eigenvectors, z, satisfies
ZT A Z = Λ   and   ZT B Z = I ,
where Λ  is the diagonal matrix whose diagonal elements are the eigenvalues. For the problem A B z = λ z  we correspondingly have
Z-1 A Z-T = Λ   and   ZT B Z = I ,
and for B A z = λ z  we have
ZT A Z = Λ   and   ZT B-1 Z = I .

4  References

Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J J, Du Croz J J, Greenbaum A, Hammarling S, McKenney A and Sorensen D (1999) LAPACK Users' Guide (3rd Edition) SIAM, Philadelphia http://www.netlib.org/lapack/lug
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

5  Arguments

1:     orderNag_OrderTypeInput
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by order=Nag_RowMajor. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2:     itypeIntegerInput
On entry: specifies the problem type to be solved.
itype=1
Az=λBz.
itype=2
ABz=λz.
itype=3
BAz=λz.
Constraint: itype=1, 2 or 3.
3:     jobNag_JobTypeInput
On entry: indicates whether eigenvectors are computed.
job=Nag_EigVals
Only eigenvalues are computed.
job=Nag_DoBoth
Eigenvalues and eigenvectors are computed.
Constraint: job=Nag_EigVals or Nag_DoBoth.
4:     uploNag_UploTypeInput
On entry: if uplo=Nag_Upper, the upper triangles of A and B are stored.
If uplo=Nag_Lower, the lower triangles of A and B are stored.
Constraint: uplo=Nag_Upper or Nag_Lower.
5:     nIntegerInput
On entry: n, the order of the matrices A and B.
Constraint: n0.
6:     ap[dim]doubleInput/Output
Note: the dimension, dim, of the array ap must be at least max1,n×n+1/2.
On entry: the upper or lower triangle of the n by n symmetric matrix A, packed by rows or columns.
The storage of elements Aij depends on the order and uplo arguments as follows:
  • if order=Nag_ColMajor and uplo=Nag_Upper,
              Aij is stored in ap[j-1×j/2+i-1], for ij;
  • if order=Nag_ColMajor and uplo=Nag_Lower,
              Aij is stored in ap[2n-j×j-1/2+i-1], for ij;
  • if order=Nag_RowMajor and uplo=Nag_Upper,
              Aij is stored in ap[2n-i×i-1/2+j-1], for ij;
  • if order=Nag_RowMajor and uplo=Nag_Lower,
              Aij is stored in ap[i-1×i/2+j-1], for ij.
On exit: the contents of ap are destroyed.
7:     bp[dim]doubleInput/Output
Note: the dimension, dim, of the array bp must be at least max1,n×n+1/2.
On entry: the upper or lower triangle of the n by n symmetric matrix B, packed by rows or columns.
The storage of elements Bij depends on the order and uplo arguments as follows:
  • if order=Nag_ColMajor and uplo=Nag_Upper,
              Bij is stored in bp[j-1×j/2+i-1], for ij;
  • if order=Nag_ColMajor and uplo=Nag_Lower,
              Bij is stored in bp[2n-j×j-1/2+i-1], for ij;
  • if order=Nag_RowMajor and uplo=Nag_Upper,
              Bij is stored in bp[2n-i×i-1/2+j-1], for ij;
  • if order=Nag_RowMajor and uplo=Nag_Lower,
              Bij is stored in bp[i-1×i/2+j-1], for ij.
On exit: the triangular factor U or L from the Cholesky factorization B=UTU or B=LLT, in the same storage format as B.
8:     w[n]doubleOutput
On exit: the eigenvalues in ascending order.
9:     z[dim]doubleOutput
Note: the dimension, dim, of the array z must be at least
  • max1,pdz×n when job=Nag_DoBoth;
  • 1 otherwise.
The i,jth element of the matrix Z is stored in
  • z[j-1×pdz+i-1] when order=Nag_ColMajor;
  • z[i-1×pdz+j-1] when order=Nag_RowMajor.
On exit: if job=Nag_DoBoth, z contains the matrix Z of eigenvectors. The eigenvectors are normalized as follows:
  • if itype=1 or 2, ZTBZ=I;
  • if itype=3, ZTB-1Z=I.
If job=Nag_EigVals, z is not referenced.
10:   pdzIntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array z.
Constraints:
  • if job=Nag_DoBoth, pdzmax1,n;
  • otherwise pdz1.
11:   failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_CONVERGENCE
The algorithm failed to converge; value off-diagonal elements of an intermediate tridiagonal form did not converge to zero.
NE_ENUM_INT_2
On entry, job=value, n=value and pdz=value.
Constraint: if job=Nag_DoBoth, pdzmax1,n;
otherwise pdz1.
On entry, job=value, pdz=value, n=value.
Constraint: if job=Nag_DoBoth, pdzmax1,n;
otherwise pdz1.
NE_INT
On entry, itype=value.
Constraint: itype=1, 2 or 3.
On entry, n=value.
Constraint: n0.
On entry, pdz=value.
Constraint: pdz>0.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
NE_MAT_NOT_POS_DEF
If fail.errnum=n+value, for 1valuen, then the leading minor of order value of B is not positive definite. The factorization of B could not be completed and no eigenvalues or eigenvectors were computed.

7  Accuracy

If B is ill-conditioned with respect to inversion, then the error bounds for the computed eigenvalues and vectors may be large, although when the diagonal elements of B differ widely in magnitude the eigenvalues and eigenvectors may be less sensitive than the condition of B would suggest. See Section 4.10 of Anderson et al. (1999) for details of the error bounds.
The example program below illustrates the computation of approximate error bounds.

8  Parallelism and Performance

nag_dspgvd (f08tcc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_dspgvd (f08tcc) makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the Users' Note for your implementation for any additional implementation-specific information.

9  Further Comments

The total number of floating-point operations is proportional to n3 .
The complex analogue of this function is nag_zhpgvd (f08tqc).

10  Example

This example finds all the eigenvalues and eigenvectors of the generalized symmetric eigenproblem ABz=λz , where
A = 0.24 0.39 0.42 -0.16 0.39 -0.11 0.79 0.63 0.42 0.79 -0.25 0.48 -0.16 0.63 0.48 -0.03   and   B = 4.16 -3.12 0.56 -0.10 -3.12 5.03 -0.83 1.09 0.56 -0.83 0.76 0.34 -0.10 1.09 0.34 1.18 ,
together with an estimate of the condition number of B, and approximate error bounds for the computed eigenvalues and eigenvectors.
The example program for nag_dspgv (f08tac) illustrates solving a generalized symmetric eigenproblem of the form Az = λ Bz .

10.1  Program Text

Program Text (f08tcce.c)

10.2  Program Data

Program Data (f08tcce.d)

10.3  Program Results

Program Results (f08tcce.r)


nag_dspgvd (f08tcc) (PDF version)
f08 Chapter Contents
f08 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2014