F04 Chapter Contents
F04 Chapter Introduction
NAG Library Manual

# NAG Library Routine DocumentF04BGF

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

## 1  Purpose

F04BGF computes the solution to a real system of linear equations $AX=B$, where $A$ is an $n$ by $n$ symmetric positive definite tridiagonal matrix and $X$ and $B$ are $n$ by $r$ matrices. An estimate of the condition number of $A$ and an error bound for the computed solution are also returned.

## 2  Specification

 SUBROUTINE F04BGF ( N, NRHS, D, E, B, LDB, RCOND, ERRBND, IFAIL)
 INTEGER N, NRHS, LDB, IFAIL REAL (KIND=nag_wp) D(*), E(*), B(LDB,*), RCOND, ERRBND

## 3  Description

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

## 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 http://www.netlib.org/lapack/lug
Higham N J (2002) Accuracy and Stability of Numerical Algorithms (2nd Edition) SIAM, Philadelphia

## 5  Parameters

1:     N – INTEGERInput
On entry: the number of linear equations $n$, i.e., the order of the matrix $A$.
Constraint: ${\mathbf{N}}\ge 0$.
2:     NRHS – INTEGERInput
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:     D($*$) – REAL (KIND=nag_wp) arrayInput/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 $LD{L}^{\mathrm{T}}$ factorization of $A$.
4:     E($*$) – REAL (KIND=nag_wp) arrayInput/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 $LD{L}^{\mathrm{T}}$ factorization of $A$. (E can also be regarded as the superdiagonal of the unit upper bidiagonal factor $U$ from the ${U}^{\mathrm{T}}DU$ factorization of $A$.)
5:     B(LDB,$*$) – REAL (KIND=nag_wp) arrayInput/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 $n$ by $r$ matrix of right-hand sides $B$.
On exit: if ${\mathbf{IFAIL}}={\mathbf{0}}$ or $\mathbf{N}+{\mathbf{1}}$, the $n$ by $r$ solution matrix $X$.
6:     LDB – INTEGERInput
On entry: the first dimension of the array B as declared in the (sub)program from which F04BGF is called.
Constraint: ${\mathbf{LDB}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
7:     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:     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, then ERRBND is returned as unity.
9:     IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to $0$, $-1\text{​ or ​}1$. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value $-1\text{​ or ​}1$ is recommended. If the output of error messages is undesirable, then the value $1$ is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is $0$. When the value $-\mathbf{1}\text{​ or ​}\mathbf{1}$ 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).

## 6  Error Indicators and Warnings

If on entry ${\mathbf{IFAIL}}={\mathbf{0}}$ or $-{\mathbf{1}}$, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Errors or warnings detected by the routine:
If ${\mathbf{IFAIL}}=-i$, the $i$th argument had an illegal value.
${\mathbf{IFAIL}}=-999$
Allocation of memory 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.
If ${\mathbf{IFAIL}}=i$, the leading minor of order $i$ of $A$ is not positive definite. The factorization could not be completed, and the solution has not been computed.
${\mathbf{IFAIL}}={\mathbf{N}}+1$
RCOND is less than machine precision, so that the matrix $A$ is numerically singular. A solution to the equations $AX=B$ has nevertheless been computed.

## 7  Accuracy

The computed solution for a single right-hand side, $\stackrel{^}{x}$, satisfies an equation of the form
 $A+E x^=b,$
where
 $E1=Oε A1$
and $\epsilon$ is the machine precision. An approximate error bound for the computed solution is given by
 $x^-x1 x1 ≤ κA E1 A1 ,$
where $\kappa \left(A\right)={‖{A}^{-1}‖}_{1}{‖A‖}_{1}$, the condition number of $A$ with respect to the solution of the linear equations. F04BGF uses the approximation ${‖E‖}_{1}=\epsilon {‖A‖}_{1}$ to estimate ERRBND. See Section 4.4 of Anderson et al. (1999) for further details.

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 complex analogue of F04BGF is F04CGF.

## 9  Example

This example solves the equations
 $AX=B,$
where $A$ is the symmetric positive definite tridiagonal matrix
 $A= 4.0 -2.0 0 0 0 -2.0 10.0 -6.0 0 0 0 -6.0 29.0 15.0 0 0 0 15.0 25.0 8.0 0 0 0 8.0 5.0 and B= 6.0 10.0 9.0 4.0 2.0 9.0 14.0 65.0 7.0 23.0 .$
An estimate of the condition number of $A$ and an approximate error bound for the computed solutions are also printed.

### 9.1  Program Text

Program Text (f04bgfe.f90)

### 9.2  Program Data

Program Data (f04bgfe.d)

### 9.3  Program Results

Program Results (f04bgfe.r)