Integer, Intent (In)	::	n, nrhs, ldb
Integer, Intent (Inout)	::	ifail
Integer, Intent (Out)	::	ipiv(n)
Real (Kind=nag_wp), Intent (Out)	::	rcond, errbnd
Complex (Kind=nag_wp), Intent (Inout)	::	dl(), d(), du(), b(ldb,)
Complex (Kind=nag_wp), Intent (Out)	::	du2(n-2)

C Header Interface

#include <nag.h>

void	f04ccf_ (const Integer n, const Integer nrhs, Complex dl[], Complex d[], Complex du[], Complex du2[], Integer ipiv[], Complex b[], const Integer ldb, double rcond, double errbnd, Integer ifail)

The routine may be called by the names f04ccf or nagf_linsys_complex_tridiag_solve.

3 Description

The

L U

decomposition with partial pivoting and row interchanges is used to factor

A

A = P L U

, where

P

is a permutation matrix,

L

is unit lower triangular with at most one nonzero subdiagonal element, and

U

is an upper triangular band matrix with two superdiagonals. The factored form of

A

is then used to solve the system of equations

A X = B

Note that the equations

A^{T} X = B

may be solved by interchanging the order of the arguments du and dl.

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

Higham N J (2002) Accuracy and Stability of Numerical Algorithms (2nd Edition) SIAM, Philadelphia

5 Arguments

1: $n$ – Integer Input: On entry: the number of linear equations $n$ , i.e., the order of the matrix $A$ .

Constraint: $n \geq 0$ .
2: $nrhs$ – Integer Input: On entry: the number of right-hand sides $r$ , i.e., the number of columns of the matrix $B$ .

Constraint: $nrhs \geq 0$ .
3: $dl (*)$ – Complex (Kind=nag_wp) array Input/Output: Note: the dimension of the array dl must be at least $\max (1, n - 1)$ .

On entry: must contain the $(n - 1)$ subdiagonal elements of the matrix $A$ .

On exit: if $ifail \geq 0$ , dl is overwritten by the $(n - 1)$ multipliers that define the matrix $L$ from the $L U$ factorization of $A$ .
4: $d (*)$ – Complex (Kind=nag_wp) array Input/Output: Note: the dimension of the array d must be at least $\max (1, n)$ .

On entry: must contain the $n$ diagonal elements of the matrix $A$ .

On exit: if $ifail \geq 0$ , d is overwritten by the $n$ diagonal elements of the upper triangular matrix $U$ from the $L U$ factorization of $A$ .
5: $du (*)$ – Complex (Kind=nag_wp) array Input/Output: Note: the dimension of the array du must be at least $\max (1, n - 1)$ .

On entry: must contain the $(n - 1)$ superdiagonal elements of the matrix $A$

On exit: if $ifail \geq 0$ , du is overwritten by the $(n - 1)$ elements of the first superdiagonal of $U$ .
6: $du2 (n - 2)$ – Complex (Kind=nag_wp) array Output: On exit: if $ifail \geq 0$ , du2 returns the $(n - 2)$ elements of the second superdiagonal of $U$ .
7: $ipiv (n)$ – Integer array Output: On exit: if $ifail \geq 0$ , the pivot indices that define the permutation matrix $P$ ; at the $i$ th step row $i$ of the matrix was interchanged with row $ipiv (i)$ . $ipiv (i)$ will always be either $i$ or $(i + 1)$ ; $ipiv (i) = i$ indicates a row interchange was not required.
8: $b (ldb, *)$ – Complex (Kind=nag_wp) array Input/Output: Note: the second dimension of the array b must be at least $\max (1, nrhs)$ .

On entry: the $n \times r$ matrix of right-hand sides $B$ .

On exit: if $ifail = 0$ or $n + 1$ , the $n \times r$ solution matrix $X$ .
9: $ldb$ – Integer Input: On entry: the first dimension of the array b as declared in the (sub)program from which f04ccf is called.

Constraint: $ldb \geq \max (1, n)$ .
10: $rcond$ – Real (Kind=nag_wp) Output: On exit: if no constraints are violated, an estimate of the reciprocal of the condition number of the matrix $A$ , computed as $rcond = 1 / ({‖ A ‖}_{1} {‖ A^{- 1} ‖}_{1})$ .
11: $errbnd$ – Real (Kind=nag_wp) Output: On exit: if $ifail = 0$ or $n + 1$ , an estimate of the forward error bound for a computed solution $\hat{x}$ , such that ${‖ \hat{x} - x ‖}_{1} / {‖ x ‖}_{1} \leq errbnd$ , where $\hat{x}$ is a column of the computed solution returned in the array b and $x$ is the corresponding column of the exact solution $X$ . If rcond is less than machine precision, errbnd is returned as unity.
12: $ifail$ – Integer Input/Output: On entry: ifail must be set to $0$ , $−1$ or $1$ to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $−1$ means that an error message is printed while a value of $1$ means that it is not.

If halting is not appropriate, the value $−1$ or $1$ is recommended. If message printing is undesirable, then the value $1$ is recommended. Otherwise, the value $0$ is recommended. When the value $- 1$ or $1$ is used it is essential to test the value of ifail on exit.

On exit: $ifail = 0$ unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

If on entry

ifail = 0

−1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

$ifail > 0 and ifail \leq n$: Diagonal element $⟨ value ⟩$ of the upper triangular factor is zero. The factorization has been completed, but the solution could not be computed.

$ifail = n + 1$: A solution has been computed, but rcond is less than machine precision so that the matrix $A$ is numerically singular.

$ifail = - 1$: On entry, $n = ⟨ value ⟩$ .
Constraint: $n \geq 0$ .

$ifail = - 2$: On entry, $nrhs = ⟨ value ⟩$ .
Constraint: $nrhs \geq 0$ .

$ifail = - 9$: On entry, $ldb = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $ldb \geq \max (1, n)$ .

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.

$ifail = - 999$: Dynamic memory allocation failed.
The complex allocatable memory required is $2 \times n$ . In this case the factorization and the solution $X$ have been computed, but rcond and errbnd have not been computed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

The computed solution for a single right-hand side,

\hat{x}

, satisfies an equation of the form

(A + E) \hat{x} = b,

where

{‖ E ‖}_{1} = O (ε) {‖ A ‖}_{1}

and

ε

is the machine precision. An approximate error bound for the computed solution is given by

\frac{{‖ \hat{x} - x ‖}_{1}}{{‖ x ‖}_{1}} \leq κ (A) \frac{{‖ E ‖}_{1}}{{‖ A ‖}_{1}},

where

κ (A) = {‖ A^{- 1} ‖}_{1} {‖ A ‖}_{1}

, the condition number of

A

with respect to the solution of the linear equations. f04ccf uses the approximation

{‖ E ‖}_{1} = ε {‖ A ‖}_{1}

to estimate errbnd. See Section 4.4 of Anderson et al. (1999) for further details.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

f04ccf 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 routine. 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 required to solve the equations

A X = B

is proportional to

n r

. The condition number estimation typically requires between four and five solves and never more than eleven solves, following the factorization.

In practice the condition number estimator is very reliable, but it can underestimate the true condition number; see Section 15.3 of Higham (2002) for further details.

The real analogue of f04ccf is f04bcf.

10 Example

This example solves the equations

A X = B,

where

A

is the tridiagonal matrix

A = (\begin{array}{r} - 1.3 + 1.3 i & 2.0 - 1.0 i & 0 & 0 & 0 \\ 1.0 - 2.0 i & - 1.3 + 1.3 i & 2.0 + 1.0 i & 0 & 0 \\ 0 & 1.0 + 1.0 i & - 1.3 + 3.3 i & - 1.0 + 1.0 i & 0 \\ 0 & 0 & 2.0 - 3.0 i & - 0.3 + 4.3 i & 1.0 - 1.0 i \\ 0 & 0 & 0 & 1.0 + 1.0 i & - 3.3 + 1.3 i \end{array})

and

B = (\begin{array}{r} 2.4 - 5.0 i & 2.7 + 6.9 i \\ 3.4 + 18.2 i & - 6.9 - 5.3 i \\ - 14.7 + 9.7 i & - 6.0 - 0.6 i \\ 31.9 - 7.7 i & - 3.9 + 9.3 i \\ - 1.0 + 1.6 i & - 3.0 + 12.2 i \end{array}) .

An estimate of the condition number of

A

and an approximate error bound for the computed solutions are also printed.

f04cc: FL CL CPP AD PY MB

NAG FL Interfacef04ccf (complex_​tridiag_​solve)

▸▿ 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 FL Interface
f04ccf (complex_tridiag_solve)