NAG FL Interfacef04cgf (complex_​posdef_​tridiag_​solve)

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1Purpose

f04cgf computes the solution to a complex system of linear equations $AX=B$, where $A$ is an $n×n$ Hermitian positive definite tridiagonal matrix and $X$ and $B$ are $n×r$ matrices. An estimate of the condition number of $A$ and an error bound for the computed solution are also returned.

2Specification

Fortran Interface
 Subroutine f04cgf ( n, nrhs, d, e, b, ldb,
 Integer, Intent (In) :: n, nrhs, ldb Integer, Intent (Inout) :: ifail Real (Kind=nag_wp), Intent (Inout) :: d(*) Real (Kind=nag_wp), Intent (Out) :: rcond, errbnd Complex (Kind=nag_wp), Intent (Inout) :: e(*), b(ldb,*)
C Header Interface
#include <nag.h>
 void f04cgf_ (const Integer *n, const Integer *nrhs, double d[], Complex e[], Complex b[], const Integer *ldb, double *rcond, double *errbnd, Integer *ifail)
The routine may be called by the names f04cgf or nagf_linsys_complex_posdef_tridiag_solve.

3Description

$A$ is factorized as $A=LD{L}^{\mathrm{H}}$, where $L$ is a unit lower bidiagonal matrix and $D$ is a real diagonal matrix, and the factored form of $A$ is then used to solve the system of equations.

4References

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

5Arguments

1: $\mathbf{n}$Integer Input
On entry: the number of linear equations $n$, i.e., the order of the matrix $A$.
Constraint: ${\mathbf{n}}\ge 0$.
2: $\mathbf{nrhs}$Integer Input
On entry: the number of right-hand sides $r$, i.e., the number of columns of the matrix $B$.
Constraint: ${\mathbf{nrhs}}\ge 0$.
3: $\mathbf{d}\left(*\right)$Real (Kind=nag_wp) array Input/Output
Note: the dimension of the array d must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry: must contain the $n$ diagonal elements of the tridiagonal matrix $A$.
On exit: if ${\mathbf{ifail}}={\mathbf{0}}$ or $\mathbf{n}+{\mathbf{1}}$, d is overwritten by the $n$ diagonal elements of the diagonal matrix $D$ from the $LD{L}^{\mathrm{H}}$ factorization of $A$.
4: $\mathbf{e}\left(*\right)$Complex (Kind=nag_wp) array Input/Output
Note: the dimension of the array e must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}-1\right)$.
On entry: must contain the $\left(n-1\right)$ subdiagonal elements of the tridiagonal matrix $A$.
On exit: if ${\mathbf{ifail}}={\mathbf{0}}$ or $\mathbf{n}+{\mathbf{1}}$, e is overwritten by the $\left(n-1\right)$ subdiagonal elements of the unit lower bidiagonal matrix $L$ from the $LD{L}^{\mathrm{H}}$ factorization of $A$. (e can also be regarded as the conjugate of the superdiagonal of the unit upper bidiagonal factor $U$ from the ${U}^{\mathrm{H}}DU$ factorization of $A$.)
5: $\mathbf{b}\left({\mathbf{ldb}},*\right)$Complex (Kind=nag_wp) array Input/Output
Note: the second dimension of the array b must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{nrhs}}\right)$.
On entry: the $n×r$ matrix of right-hand sides $B$.
On exit: if ${\mathbf{ifail}}={\mathbf{0}}$ or $\mathbf{n}+{\mathbf{1}}$, the $n×r$ solution matrix $X$.
6: $\mathbf{ldb}$Integer Input
On entry: the first dimension of the array b as declared in the (sub)program from which f04cgf is called.
Constraint: ${\mathbf{ldb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
7: $\mathbf{rcond}$Real (Kind=nag_wp) Output
On exit: if ${\mathbf{ifail}}={\mathbf{0}}$ or $\mathbf{n}+{\mathbf{1}}$, an estimate of the reciprocal of the condition number of the matrix $A$, computed as ${\mathbf{rcond}}=1/\left({‖A‖}_{1},{‖{A}^{-1}‖}_{1}\right)$.
8: $\mathbf{errbnd}$Real (Kind=nag_wp) Output
On exit: if ${\mathbf{ifail}}={\mathbf{0}}$ or $\mathbf{n}+{\mathbf{1}}$, an estimate of the forward error bound for a computed solution $\stackrel{^}{x}$, such that ${‖\stackrel{^}{x}-x‖}_{1}/{‖x‖}_{1}\le {\mathbf{errbnd}}$, where $\stackrel{^}{x}$ 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.
9: $\mathbf{ifail}$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 $-\mathbf{1}$ or $\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit: ${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).

6Error Indicators and Warnings

If on entry ${\mathbf{ifail}}=0$ or $-1$, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
${\mathbf{ifail}}>0 \text{and} {\mathbf{ifail}}\le {\mathbf{n}}$
The principal minor of order $⟨\mathit{\text{value}}⟩$ of the matrix $A$ is not positive definite. The factorization has not been completed and the solution could not be computed.
${\mathbf{ifail}}={\mathbf{n}}+1$
A solution has been computed, but rcond is less than machine precision so that the matrix $A$ is numerically singular.
${\mathbf{ifail}}=-1$
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{n}}\ge 0$.
${\mathbf{ifail}}=-2$
On entry, ${\mathbf{nrhs}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{nrhs}}\ge 0$.
${\mathbf{ifail}}=-6$
On entry, ${\mathbf{ldb}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{ldb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
${\mathbf{ifail}}=-99$
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.
${\mathbf{ifail}}=-399$
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.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
The real allocatable memory required is 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.

7Accuracy

The computed solution for a single right-hand side, $\stackrel{^}{x}$, satisfies an equation of the form
 $(A+E) x^=b,$
where
 $‖E‖1=O(ε) ‖A‖1$
and $\epsilon$ is the machine precision. An approximate error bound for the computed solution is given by
 $‖x^-x‖1 ‖x‖1 ≤ κ(A) ‖E‖1 ‖A‖1 ,$
where $\kappa \left(A\right)={‖{A}^{-1}‖}_{1}{‖A‖}_{1}$, the condition number of $A$ with respect to the solution of the linear equations. f04cgf uses the approximation ${‖E‖}_{1}=\epsilon {‖A‖}_{1}$ to estimate errbnd. See Section 4.4 of Anderson et al. (1999) for further details.

8Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
f04cgf 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.

9Further Comments

The total number of floating-point operations required to solve the equations $AX=B$ is proportional to $nr$. The condition number estimation requires $\mathit{O}\left(n\right)$ floating-point operations.
See Section 15.3 of Higham (2002) for further details on computing the condition number of tridiagonal matrices.
The real analogue of f04cgf is f04bgf.

10Example

This example solves the equations
 $AX=B,$
where $A$ is the Hermitian positive definite tridiagonal matrix
 $A= ( 16.0i+00.0 16.0+16.0i 0.0i+0.0 0.0i+0.0 16.0-16.0i 41.0i+00.0 18.0-9.0i 0.0i+0.0 0.0i+00.0 18.0+09.0i 46.0i+0.0 1.0-4.0i 0.0i+00.0 0.0i+00.0 1.0+4.0i 21.0i+0.0 )$
and
 $B= ( 64.0+16.0i -16.0-32.0i 93.0+62.0i 61.0-66.0i 78.0-80.0i 71.0-74.0i 14.0-27.0i 35.0+15.0i ) .$
An estimate of the condition number of $A$ and an approximate error bound for the computed solutions are also printed.

10.1Program Text

Program Text (f04cgfe.f90)

10.2Program Data

Program Data (f04cgfe.d)

10.3Program Results

Program Results (f04cgfe.r)