void	nag_zgebal (Nag_OrderType order, Nag_JobType job, Integer n, Complex a[], Integer pda, Integer ilo, Integer ihi, double scale[], NagError *fail)

3

Description

nag_zgebal (f08nvc) balances a complex general matrix

A

. The term ‘balancing’ covers two steps, each of which involves a similarity transformation of

A

. The function can perform either or both of these steps.

The function first attempts to permute

A

to block upper triangular form by a similarity transformation:

P A P^{T} = A^{'} = (\begin{matrix} A_{11}^{'} & A_{12}^{'} & A_{13}^{'} \\ 0 & A_{22}^{'} & A_{23}^{'} \\ 0 & 0 & A_{33}^{'} \end{matrix})

where

P

is a permutation matrix, and

A_{11}^{'}

and

A_{33}^{'}

are upper triangular. Then the diagonal elements of

A_{11}^{'}

and

A_{33}^{'}

are eigenvalues of

A

. The rest of the eigenvalues of

A

are the eigenvalues of the central diagonal block

A_{22}^{'}

, in rows and columns

i_{lo}

i_{hi}

. Subsequent operations to compute the eigenvalues of

A

(or its Schur factorization) need only be applied to these rows and columns; this can save a significant amount of work if

i_{lo} > 1

and

i_{hi} < n

. If no suitable permutation exists (as is often the case), the function sets

i_{lo} = 1

and

i_{hi} = n

, and

A_{22}^{'}

is the whole of

A

The function applies a diagonal similarity transformation to

A^{'}

, to make the rows and columns of

A_{22}^{'}

as close in norm as possible:

A^{''} = D A^{'} D^{- 1} = (\begin{matrix} I & 0 & 0 \\ 0 & D_{22} & 0 \\ 0 & 0 & I \end{matrix}) (\begin{matrix} A_{11}^{'} & A_{12}^{'} & A_{13}^{'} \\ 0 & A_{22}^{'} & A_{23}^{'} \\ 0 & 0 & A_{33}^{'} \end{matrix}) (\begin{matrix} I & 0 & 0 \\ 0 & D_{22}^{- 1} & 0 \\ 0 & 0 & I \end{matrix}) .

This scaling can reduce the norm of the matrix (i.e.,

‖A_{22}^{''}‖ < ‖A_{22}^{'}‖

) and hence reduce the effect of rounding errors on the accuracy of computed eigenvalues and eigenvectors.

4

References

Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

5

Arguments

1: $order$ – Nag_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 3.3.1.3 in How to Use the NAG Library and its Documentation for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

Nag_ColMajor

2: $job$ – Nag_JobTypeInput

On entry: indicates whether

A

is to be permuted and/or scaled (or neither).

$job = Nag_DoNothing$: $A$ is neither permuted nor scaled (but values are assigned to ilo, ihi and scale).
$job = Nag_Permute$: $A$ is permuted but not scaled.
$job = Nag_Scale$: $A$ is scaled but not permuted.
$job = Nag_DoBoth$: $A$ is both permuted and scaled.

Constraint:

job = Nag_DoNothing

Nag_Permute

Nag_Scale

Nag_DoBoth

3: $n$ – IntegerInput

On entry:

n

, the order of the matrix

A

Constraint:

n \geq 0

4: $a [\dim]$ – ComplexInput/Output

Note: the dimension, dim, of the array a must be at least

\max (1, pda \times n)

Where

A (i, j)

appears in this document, it refers to the array element

$a [(j - 1) \times pda + i - 1]$ when $order = Nag_ColMajor$ ;
$a [(i - 1) \times pda + j - 1]$ when $order = Nag_RowMajor$ .

On entry: the

n

n

matrix

A

On exit: a is overwritten by the balanced matrix. If

job = Nag_DoNothing

, a is not referenced.

5: $pda$ – IntegerInput

On entry: the stride separating row or column elements (depending on the value of order) in the array a.

Constraint:

pda \geq \max (1, n)

6: $ilo$ – Integer *Output

7: $ihi$ – Integer *Output

On exit: the values

i_{lo}

and

i_{hi}

such that on exit

A (i, j)

is zero if

i > j

and

1 \leq j < i_{lo}

i_{hi} < i \leq n

job = Nag_DoNothing

Nag_Scale

i_{lo} = 1

and

i_{hi} = n

8: $scale [n]$ – doubleOutput

On exit: details of the permutations and scaling factors applied to

A

. More precisely, if

p_{j}

is the index of the row and column interchanged with row and column

j

and

d_{j}

is the scaling factor used to balance row and column

j

then

scale [j - 1] = \{\begin{cases} p_{j}, & j = 1, 2, \dots, i_{lo} - 1 \\ d_{j}, & j = i_{lo}, i_{lo} + 1, \dots, i_{hi} and \\ p_{j}, & j = i_{hi} + 1, i_{hi} + 2, \dots, n . \end{cases}

The order in which the interchanges are made is

n

i_{hi} + 1

then

1

i_{lo} - 1

9: $fail$ – NagError *Input/Output

The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

6

Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_INT: On entry, $n = 〈value〉$ .
Constraint: $n \geq 0$ .

On entry, $pda = 〈value〉$ .
Constraint: $pda > 0$ .
NE_INT_2: On entry, $pda = 〈value〉$ and $n = 〈value〉$ .
Constraint: $pda \geq \max (1, n)$ .
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 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.

7

Accuracy

The errors are negligible, compared with those in subsequent computations.

8

Parallelism and Performance

nag_zgebal (f08nvc) 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

If the matrix

A

is balanced by nag_zgebal (f08nvc), then any eigenvectors computed subsequently are eigenvectors of the matrix

A^{''}

(see Section 3) and hence nag_zgebak (f08nwc) must then be called to transform them back to eigenvectors of

A

If the Schur vectors of

A

are required, then this function must not be called with

job = Nag_Scale

Nag_DoBoth

, because then the balancing transformation is not unitary. If this function is called with

job = Nag_Permute

, then any Schur vectors computed subsequently are Schur vectors of the matrix

A^{''}

, and nag_zgebak (f08nwc) must be called (with

side = Nag_RightSide

) to transform them back to Schur vectors of

A

The total number of real floating-point operations is approximately proportional to

n^{2}

The real analogue of this function is nag_dgebal (f08nhc).

10

Example

This example computes all the eigenvalues and right eigenvectors of the matrix

A

, where

A = (\begin{array}{r} 1.50 - 2.75 i & 0.00 + 0.00 i & 0.00 + 0.00 i & 0.00 + 0.00 i \\ - 8.06 - 1.24 i & - 2.50 - 0.50 i & 0.00 + 0.00 i & - 0.75 + 0.50 i \\ - 2.09 + 7.56 i & 1.39 + 3.97 i & - 1.25 + 0.75 i & - 4.82 - 5.67 i \\ 6.18 + 9.79 i & - 0.92 - 0.62 i & 0.00 + 0.00 i & - 2.50 - 0.50 i \end{array}) .

The program first calls nag_zgebal (f08nvc) to balance the matrix; it then computes the Schur factorization of the balanced matrix, by reduction to Hessenberg form and the

Q R

algorithm. Then it calls nag_ztrevc (f08qxc) to compute the right eigenvectors of the balanced matrix, and finally calls nag_zgebak (f08nwc) to transform the eigenvectors back to eigenvectors of the original matrix

A

NAG Library Function Document

nag_zgebal (f08nvc)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1

Purpose

2

Specification

3

Description

4

References

5

Arguments

6

Error Indicators and Warnings

7

Accuracy

8

Parallelism and Performance

9

Further Comments

10

Example

10.1

Program Text

10.2

Program Data

10.3

Program Results