NAG CL Interface
f08wpc (zggevx)

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1 Purpose

f08wpc computes for a pair of n×n complex nonsymmetric matrices (A,B) the generalized eigenvalues and, optionally, the left and/or right generalized eigenvectors using the QZ algorithm.
Optionally it also computes a balancing transformation to improve the conditioning of the eigenvalues and eigenvectors, reciprocal condition numbers for the eigenvalues, and reciprocal condition numbers for the right eigenvectors.

2 Specification

#include <nag.h>
void  f08wpc (Nag_OrderType order, Nag_BalanceType balanc, Nag_LeftVecsType jobvl, Nag_RightVecsType jobvr, Nag_RCondType sense, Integer n, Complex a[], Integer pda, Complex b[], Integer pdb, Complex alpha[], Complex beta[], Complex vl[], Integer pdvl, Complex vr[], Integer pdvr, Integer *ilo, Integer *ihi, double lscale[], double rscale[], double *abnrm, double *bbnrm, double rconde[], double rcondv[], NagError *fail)
The function may be called by the names: f08wpc, nag_lapackeig_zggevx or nag_zggevx.

3 Description

A generalized eigenvalue for a pair of matrices (A,B) is a scalar λ or a ratio α/β=λ, such that A-λB is singular. It is usually represented as the pair (α,β), as there is a reasonable interpretation for β=0, and even for both being zero.
The right generalized eigenvector vj corresponding to the generalized eigenvalue λj of (A,B) satisfies
A vj = λj B vj .  
The left generalized eigenvector uj corresponding to the generalized eigenvalue λj of (A,B) satisfies
ujH A = λj ujH B ,  
where ujH is the conjugate-transpose of uj.
All the eigenvalues and, if required, all the eigenvectors of the complex generalized eigenproblem Ax=λBx, where A and B are complex, square matrices, are determined using the QZ algorithm. The complex QZ algorithm consists of three stages:
  1. 1.A is reduced to upper Hessenberg form (with real, non-negative subdiagonal elements) and at the same time B is reduced to upper triangular form.
  2. 2.A is further reduced to triangular form while the triangular form of B is maintained and the diagonal elements of B are made real and non-negative. This is the generalized Schur form of the pair (A,B) .
    This function does not actually produce the eigenvalues λj, but instead returns αj and βj such that
    λj=αj/βj,  j=1,2,,n.  
    The division by βj becomes your responsibility, since βj may be zero, indicating an infinite eigenvalue.
  3. 3.If the eigenvectors are required they are obtained from the triangular matrices and then transformed back into the original coordinate system.
For details of the balancing option, see Section 3 in f08wvc.

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 https://www.netlib.org/lapack/lug
Golub G H and Van Loan C F (2012) Matrix Computations (4th Edition) Johns Hopkins University Press, Baltimore
Wilkinson J H (1979) Kronecker's canonical form and the QZ algorithm Linear Algebra Appl. 28 285–303

5 Arguments

1: order Nag_OrderType Input
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 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2: balanc Nag_BalanceType Input
On entry: specifies the balance option to be performed.
balanc=Nag_NoBalancing
Do not diagonally scale or permute.
balanc=Nag_BalancePermute
Permute only.
balanc=Nag_BalanceScale
Scale only.
balanc=Nag_BalanceBoth
Both permute and scale.
Computed reciprocal condition numbers will be for the matrices after permuting and/or balancing. Permuting does not change condition numbers (in exact arithmetic), but balancing does. In the absence of other information, balanc=Nag_BalanceBoth is recommended.
Constraint: balanc=Nag_NoBalancing, Nag_BalancePermute, Nag_BalanceScale or Nag_BalanceBoth.
3: jobvl Nag_LeftVecsType Input
On entry: if jobvl=Nag_NotLeftVecs, do not compute the left generalized eigenvectors.
If jobvl=Nag_LeftVecs, compute the left generalized eigenvectors.
Constraint: jobvl=Nag_NotLeftVecs or Nag_LeftVecs.
4: jobvr Nag_RightVecsType Input
On entry: if jobvr=Nag_NotRightVecs, do not compute the right generalized eigenvectors.
If jobvr=Nag_RightVecs, compute the right generalized eigenvectors.
Constraint: jobvr=Nag_NotRightVecs or Nag_RightVecs.
5: sense Nag_RCondType Input
On entry: determines which reciprocal condition numbers are computed.
sense=Nag_NotRCond
None are computed.
sense=Nag_RCondEigVals
Computed for eigenvalues only.
sense=Nag_RCondEigVecs
Computed for eigenvectors only.
sense=Nag_RCondBoth
Computed for eigenvalues and eigenvectors.
Constraint: sense=Nag_NotRCond, Nag_RCondEigVals, Nag_RCondEigVecs or Nag_RCondBoth.
6: n Integer Input
On entry: n, the order of the matrices A and B.
Constraint: n0.
7: a[dim] Complex Input/Output
Note: the dimension, dim, of the array a must be at least max(1,pda×n).
where A(i,j) appears in this document, it refers to the array element
  • a[(j-1)×pda+i-1] when order=Nag_ColMajor;
  • a[(i-1)×pda+j-1] when order=Nag_RowMajor.
On entry: the matrix A in the pair (A,B).
On exit: a has been overwritten. If jobvl=Nag_LeftVecs or jobvr=Nag_RightVecs or both, then A contains the first part of the Schur form of the ‘balanced’ versions of the input A and B.
8: pda Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array a.
Constraint: pdamax(1,n).
9: b[dim] Complex Input/Output
Note: the dimension, dim, of the array b must be at least max(1,pdb×n).
where B(i,j) appears in this document, it refers to the array element
  • b[(j-1)×pdb+i-1] when order=Nag_ColMajor;
  • b[(i-1)×pdb+j-1] when order=Nag_RowMajor.
On entry: the matrix B in the pair (A,B).
On exit: b has been overwritten.
10: pdb Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array b.
Constraint: pdbmax(1,n).
11: alpha[n] Complex Output
On exit: see the description of beta.
12: beta[n] Complex Output
On exit: alpha[j-1]/beta[j-1], for j=1,2,,n, will be the generalized eigenvalues.
Note:  the quotients alpha[j-1]/beta[j-1] may easily overflow or underflow, and beta[j-1] may even be zero. Thus, you should avoid naively computing the ratio αj/βj. However, max(|αj|) will always be less than and usually comparable with A2 in magnitude, and max(|βj|) will always be less than and usually comparable with B2.
13: vl[dim] Complex Output
Note: the dimension, dim, of the array vl must be at least
  • max(1,pdvl×n) when jobvl=Nag_LeftVecs;
  • 1 otherwise.
The (i,j)th element of the matrix is stored in
  • vl[(j-1)×pdvl+i-1] when order=Nag_ColMajor;
  • vl[(i-1)×pdvl+j-1] when order=Nag_RowMajor.
On exit: if jobvl=Nag_LeftVecs, the left generalized eigenvectors uj are stored one after another in the columns of vl, in the same order as the corresponding eigenvalues. Each eigenvector will be scaled so the largest component will have |real part|+|imag. part|=1.
If jobvl=Nag_NotLeftVecs, vl is not referenced.
14: pdvl Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array vl.
Constraints:
  • if jobvl=Nag_LeftVecs, pdvl max(1,n) ;
  • otherwise pdvl1.
15: vr[dim] Complex Output
Note: the dimension, dim, of the array vr must be at least
  • max(1,pdvr×n) when jobvr=Nag_RightVecs;
  • 1 otherwise.
The (i,j)th element of the matrix is stored in
  • vr[(j-1)×pdvr+i-1] when order=Nag_ColMajor;
  • vr[(i-1)×pdvr+j-1] when order=Nag_RowMajor.
On exit: if jobvr=Nag_RightVecs, the right generalized eigenvectors vj are stored one after another in the columns of vr, in the same order as the corresponding eigenvalues. Each eigenvector will be scaled so the largest component will have |real part|+|imag. part|=1.
If jobvr=Nag_NotRightVecs, vr is not referenced.
16: pdvr Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array vr.
Constraints:
  • if jobvr=Nag_RightVecs, pdvr max(1,n) ;
  • otherwise pdvr1.
17: ilo Integer * Output
18: ihi Integer * Output
On exit: ilo and ihi are integer values such that A(i,j)=0 and B(i,j)=0 if i>j and j=1,2,,ilo-1 or i=ihi+1,,n.
If balanc=Nag_NoBalancing or Nag_BalanceScale, ilo=1 and ihi=n.
19: lscale[n] double Output
On exit: details of the permutations and scaling factors applied to the left side of A and B.
If plj is the index of the row interchanged with row j, and dlj is the scaling factor applied to row j, then:
  • lscale[j-1] = plj , for j=1,2,,ilo-1;
  • lscale = dlj , for j=ilo,,ihi;
  • lscale = plj , for j=ihi+1,,n.
The order in which the interchanges are made is n to ihi+1, then 1 to ilo-1.
20: rscale[n] double Output
On exit: details of the permutations and scaling factors applied to the right side of A and B.
If prj is the index of the column interchanged with column j, and drj is the scaling factor applied to column j, then:
  • rscale[j-1]=prj, for j=1,2,,ilo-1;
  • if rscale=drj, for j=ilo,,ihi;
  • if rscale=prj, for j=ihi+1,,n.
The order in which the interchanges are made is n to ihi+1, then 1 to ilo-1.
21: abnrm double * Output
On exit: the 1-norm of the balanced matrix A.
22: bbnrm double * Output
On exit: the 1-norm of the balanced matrix B.
23: rconde[dim] double Output
Note: the dimension, dim, of the array rconde must be at least max(1,n).
On exit: if sense=Nag_RCondEigVals or Nag_RCondBoth, the reciprocal condition numbers of the eigenvalues, stored in consecutive elements of the array.
If sense=Nag_NotRCond or Nag_RCondEigVecs, rconde is not referenced.
24: rcondv[dim] double Output
Note: the dimension, dim, of the array rcondv must be at least max(1,n).
On exit: if sense=Nag_RCondEigVecs or Nag_RCondBoth, the estimated reciprocal condition numbers of the selected eigenvectors, stored in consecutive elements of the array.
If sense=Nag_NotRCond or Nag_RCondEigVals, rcondv is not referenced.
25: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_EIGENVECTORS
A failure occurred in f08yxc while computing generalized eigenvectors.
NE_ENUM_INT_2
On entry, jobvl=value, pdvl=value and n=value.
Constraint: if jobvl=Nag_LeftVecs, pdvl max(1,n) ;
otherwise pdvl1.
On entry, jobvr=value, pdvr=value and n=value.
Constraint: if jobvr=Nag_RightVecs, pdvr max(1,n) ;
otherwise pdvr1.
NE_INT
On entry, n=value.
Constraint: n0.
On entry, pda=value.
Constraint: pda>0.
On entry, pdb=value.
Constraint: pdb>0.
On entry, pdvl=value.
Constraint: pdvl>0.
On entry, pdvr=value.
Constraint: pdvr>0.
NE_INT_2
On entry, pda=value and n=value.
Constraint: pdamax(1,n).
On entry, pdb=value and n=value.
Constraint: pdbmax(1,n).
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.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_ITERATION_QZ
The QZ iteration failed. No eigenvectors have been calculated but alpha and beta should be correct from element value.
The QZ iteration failed with an unexpected error, please contact NAG.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.

7 Accuracy

The computed eigenvalues and eigenvectors are exact for nearby matrices (A+E) and (B+F), where
(E,F) F = O(ε) (A,B) F ,  
and ε is the machine precision.
An approximate error bound on the chordal distance between the ith computed generalized eigenvalue w and the corresponding exact eigenvalue λ is
ε × abnrm,bbnrm2 / rconde[i-1] .  
An approximate error bound for the angle between the ith computed eigenvector uj or vj is given by
ε × abnrm,bbnrm2 / rcondv[i-1] .  
For further explanation of the reciprocal condition numbers rconde and rcondv, see Section 4.11 of Anderson et al. (1999).
Note:  interpretation of results obtained with the QZ algorithm often requires a clear understanding of the effects of small changes in the original data. These effects are reviewed in Wilkinson (1979), in relation to the significance of small values of αj and βj. It should be noted that if αj and βj are both small for any j, it may be that no reliance can be placed on any of the computed eigenvalues λi=αi/βi. You are recommended to study Wilkinson (1979) and, if in difficulty, to seek expert advice on determining the sensitivity of the eigenvalues to perturbations in the data.

8 Parallelism and Performance

f08wpc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08wpc 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 X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also 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 real analogue of this function is f08wbc.

10 Example

This example finds all the eigenvalues and right eigenvectors of the matrix pair (A,B), where
A = ( -21.10-22.50i 53.50-50.50i -34.50+127.50i 7.50+00.50i -0.46-07.78i -3.50-37.50i -15.50+058.50i -10.50-01.50i 4.30-05.50i 39.70-17.10i -68.50+012.50i -7.50-03.50i 5.50+04.40i 14.40+43.30i -32.50-046.00i -19.00-32.50i )  
and
B = ( 1.00-5.00i 1.60+1.20i -3.00+0.00i 0.00-1.00i 0.80-0.60i 3.00-5.00i -4.00+3.00i -2.40-3.20i 1.00+0.00i 2.40+1.80i -4.00-5.00i 0.00-3.00i 0.00+1.00i -1.80+2.40i 0.00-4.00i 4.00-5.00i ) ,  
together with estimates of the condition number and forward error bounds for each eigenvalue and eigenvector. The option to balance the matrix pair is used.

10.1 Program Text

Program Text (f08wpce.c)

10.2 Program Data

Program Data (f08wpce.d)

10.3 Program Results

Program Results (f08wpce.r)