NAG Library Function Document

nag_superlu_refine_lu (f11mhc)

nag_superlu_refine_lu (Nag_OrderType order, Nag_TransType trans, Integer n, const Integer icolzp[], const Integer irowix[], const double a[], const Integer iprm[], const Integer il[], const double lval[], const Integer iu[], const double uval[], Integer nrhs, const double b[], Integer pdb, double x[], Integer pdx, double ferr[], double berr[], NagError *fail)

3 Description

nag_superlu_refine_lu (f11mhc) returns the backward errors and estimated bounds on the forward errors for the solution of a real system of linear equations with multiple right-hand sides

A X = B

A^{T} X = B

. The function handles each right-hand side vector (stored as a column of the matrix

B

) independently, so we describe the function of nag_superlu_refine_lu (f11mhc) in terms of a single right-hand side

b

and solution

x

Given a computed solution

x

, the function computes the component-wise backward error

β

. This is the size of the smallest relative perturbation in each element of

A

and

b

such that if

x

is the exact solution of a perturbed system:

\begin{matrix} (A + δ A) x = b + δ b \\ then |δ a_{i j}| \leq β |a_{i j}| and |δ b_{i}| \leq β |b_{i}| . \end{matrix}

Then the function estimates a bound for the component-wise forward error in the computed solution, defined by:

\max_{i}^{} |x_{i} - {\hat{x}}_{i}| / \max_{i}^{} |x_{i}|

where

\hat{x}

is the true solution.

The function uses the

L U

factorization

P_{r} A P_{c} = L U

computed by nag_superlu_lu_factorize (f11mec) and the solution computed by nag_superlu_solve_lu (f11mfc).

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 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

Nag_ColMajor

2: trans – Nag_TransTypeInput

On entry: specifies whether

A X = B

A^{T} X = B

is solved.

$trans = Nag_NoTrans$: $A X = B$ is solved.
$trans = Nag_Trans$: $A^{T} X = B$ is solved.

Constraint:

trans = Nag_NoTrans

Nag_Trans

3: n – IntegerInput

On entry:

n

, the order of the matrix

A

Constraint:

n \geq 0

4: icolzp[ $\dim$ ] – const IntegerInput

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

n + 1

On entry:

icolzp [i - 1]

contains the index in

A

of the start of a new column. See Section 2.1.3 in the f11 Chapter Introduction.

5: irowix[ $\dim$ ] – const IntegerInput

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

icolzp [n] - 1

, the number of nonzeros of the sparse matrix

A

On entry: the row index array of sparse matrix

A

6: a[ $\dim$ ] – const doubleInput

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

icolzp [n] - 1

, the number of nonzeros of the sparse matrix

A

On entry: the array of nonzero values in the sparse matrix

A

7: iprm[ $7 \times n$ ] – const IntegerInput

On entry: the column permutation which defines

P_{c}

, the row permutation which defines

P_{r}

, plus associated data structures as computed by nag_superlu_lu_factorize (f11mec).

8: il[ $\dim$ ] – const IntegerInput

Note: the dimension, dim, of the array il must be at least as large as the dimension of the array of the same name in nag_superlu_lu_factorize (f11mec).

On entry: records the sparsity pattern of matrix

L

as computed by nag_superlu_lu_factorize (f11mec).

9: lval[ $\dim$ ] – const doubleInput

Note: the dimension, dim, of the array lval must be at least as large as the dimension of the array of the same name in nag_superlu_lu_factorize (f11mec).

On entry: records the nonzero values of matrix

L

and some nonzero values of matrix

U

as computed by nag_superlu_lu_factorize (f11mec).

10: iu[ $\dim$ ] – const IntegerInput

Note: the dimension, dim, of the array iu must be at least as large as the dimension of the array of the same name in nag_superlu_lu_factorize (f11mec).

On entry: records the sparsity pattern of matrix

U

as computed by nag_superlu_lu_factorize (f11mec).

11: uval[ $\dim$ ] – const doubleInput

Note: the dimension, dim, of the array uval must be at least as large as the dimension of the array of the same name in nag_superlu_lu_factorize (f11mec).

On entry: records some nonzero values of matrix

U

as computed by nag_superlu_lu_factorize (f11mec).

12: nrhs – IntegerInput

On entry:

nrhs

, the number of right-hand sides in

B

Constraint:

nrhs \geq 0

13: b[ $\dim$ ] – const doubleInput

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

$\max (1, pdb \times nrhs)$ when $order = Nag_ColMajor$ ;
$\max (1, n \times pdb)$ when $order = Nag_RowMajor$ .

The

(i, j)

th element of the matrix

B

is stored in

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

On entry: the

n

nrhs

right-hand side matrix

B

14: pdb – IntegerInput

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

Constraints:

if $order = Nag_ColMajor$ , $pdb \geq \max (1, n)$ ;
if $order = Nag_RowMajor$ , $pdb \geq \max (1, nrhs)$ .

15: x[ $\dim$ ] – doubleInput/Output

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

$\max (1, pdx \times nrhs)$ when $order = Nag_ColMajor$ ;
$\max (1, n \times pdx)$ when $order = Nag_RowMajor$ .

The

(i, j)

th element of the matrix

X

is stored in

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

On entry: the

n

nrhs

solution matrix

X

, as returned by nag_superlu_solve_lu (f11mfc).

On exit: the

n

nrhs

improved solution matrix

X

16: pdx – IntegerInput

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

Constraints:

if $order = Nag_ColMajor$ , $pdx \geq \max (1, n)$ ;
if $order = Nag_RowMajor$ , $pdx \geq \max (1, nrhs)$ .

17: ferr[nrhs] – doubleOutput

On exit:

ferr [j - 1]

contains an estimated error bound for the

j

th solution vector, that is, the

j

th column of

X

, for

j = 1, 2, \dots, nrhs

18: berr[nrhs] – doubleOutput

On exit:

berr [j - 1]

contains the component-wise backward error bound

β

for the

j

th solution vector, that is, the

j

th column of

X

, for

j = 1, 2, \dots, nrhs

19: fail – NagError *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_INT: On entry, $n = ⟨value⟩$ .
Constraint: $n \geq 0$ .
On entry, $nrhs = ⟨value⟩$ .
Constraint: $nrhs \geq 0$ .
On entry, $pdb = ⟨value⟩$ .
Constraint: $pdb > 0$ .
On entry, $pdx = ⟨value⟩$ .
Constraint: $pdx > 0$ .
NE_INT_2: On entry, $pdb = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: $pdb \geq \max (1, n)$ .
On entry, $pdb = ⟨value⟩$ and $nrhs = ⟨value⟩$ .
Constraint: $pdb \geq \max (1, nrhs)$ .
On entry, $pdx = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: $pdx \geq \max (1, n)$ .
On entry, $pdx = ⟨value⟩$ and $nrhs = ⟨value⟩$ .
Constraint: $pdx \geq \max (1, nrhs)$ .
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_INVALID_PERM_COL: Incorrect column permutations in array iprm.
NE_INVALID_PERM_ROW: Incorrect Row Permutations in array iprm.

7 Accuracy

The bounds returned in ferr are not rigorous, because they are estimated, not computed exactly; but in practice they almost always overestimate the actual error.

8 Parallelism and Performance

nag_superlu_refine_lu (f11mhc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

nag_superlu_refine_lu (f11mhc) 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

At most five steps of iterative refinement are performed, but usually only one or two steps are required.

Estimating the forward error involves solving a number of systems of linear equations of the form

A x = b

A^{T} x = b

;

10 Example

This example solves the system of equations

A X = B

using iterative refinement and to compute the forward and backward error bounds, where

A = (\begin{matrix} 2.00 & 1.00 & 0 & 0 & 0 \\ 0 & 0 & 1.00 & - 1.00 & 0 \\ 4.00 & 0 & 1.00 & 0 & 1.00 \\ 0 & 0 & 0 & 1.00 & 2.00 \\ 0 & - 2.00 & 0 & 0 & 3.00 \end{matrix}) and B = (\begin{matrix} 1.56 & 3.12 \\ - 0.25 & - 0.50 \\ 3.60 & 7.20 \\ 1.33 & 2.66 \\ 0.52 & 1.04 \end{matrix}) .

Here

A

is nonsymmetric and must first be factorized by nag_superlu_lu_factorize (f11mec).

NAG Library Function Documentnag_superlu_refine_lu (f11mhc)

+− 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

NAG Library Function Document

nag_superlu_refine_lu (f11mhc)