Integer, Intent (In)	::	m, n, nrhs, lda, ldb, lwork
Integer, Intent (Out)	::	info
Real (Kind=nag_wp), Intent (Inout)	::	a(lda,), b(ldb,)
Real (Kind=nag_wp), Intent (Out)	::	work(max(1,lwork))
Character (1), Intent (In)	::	trans

C Header Interface

#include nagmk26.h

void	f08aaf_ ( const char trans, const Integer m, const Integer n, const Integer nrhs, double a[], const Integer lda, double b[], const Integer ldb, double work[], const Integer lwork, Integer info, const Charlen length_trans)

The routine may be called by its LAPACK name dgels.

3

Description

The following options are provided:

trans ='N'

and

m \geq n

: find the least squares solution of an overdetermined system, i.e., solve the least squares problem

\min_{x} {‖b - A x‖}_{2} .

trans ='N'

and

m < n

: find the minimum norm solution of an underdetermined system

A x = b

trans ='T'

and

m \geq n

: find the minimum norm solution of an undetermined system

A^{T} x = b

trans ='T'

and

m < n

: find the least squares solution of an overdetermined system, i.e., solve the least squares problem

\min_{x} {‖b - A^{T} x‖}_{2} .

Several right-hand side vectors

b

and solution vectors

x

can be handled in a single call; they are stored as the columns of the

m

r

right-hand side matrix

B

and the

n

r

solution matrix

X

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

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

5

Arguments

1: $trans$ – Character(1)Input

On entry: if

trans ='N'

, the linear system involves

A

trans ='T'

, the linear system involves

A^{T}

Constraint:

trans ='N'

'T'

2: $m$ – IntegerInput

On entry:

m

, the number of rows of the matrix

A

Constraint:

m \geq 0

3: $n$ – IntegerInput

On entry:

n

, the number of columns of the matrix

A

Constraint:

n \geq 0

4: $nrhs$ – IntegerInput

On entry:

r

, the number of right-hand sides, i.e., the number of columns of the matrices

B

and

X

Constraint:

nrhs \geq 0

5: $a (lda *)$ – Real (Kind=nag_wp) arrayInput/Output

Note: the second dimension of the array a must be at least

\max (1, n)

On entry: the

m

n

matrix

A

On exit: if

m \geq n

, a is overwritten by details of its

Q R

factorization, as returned by f08aef (dgeqrf).

m < n

, a is overwritten by details of its

L Q

factorization, as returned by f08ahf (dgelqf).

6: $lda$ – IntegerInput

On entry: the first dimension of the array a as declared in the (sub)program from which f08aaf (dgels) is called.

Constraint:

lda \geq \max (1, m)

7: $b (ldb *)$ – Real (Kind=nag_wp) arrayInput/Output

Note: the second dimension of the array b must be at least

\max (1, nrhs)

On entry: the matrix

B

of right-hand side vectors, stored in columns; b is

m

r

trans ='N'

, or

n

r

trans ='T'

On exit: b is overwritten by the solution vectors,

x

, stored in columns:

if $trans ='N'$ and $m \geq n$ , or $trans ='T'$ and $m < n$ , elements $1$ to $\min (m, n)$ in each column of b contain the least squares solution vectors; the residual sum of squares for the solution is given by the sum of squares of the modulus of elements $(\min (m, n) + 1)$ to $\max (m, n)$ in that column;
otherwise, elements $1$ to $\max (m, n)$ in each column of b contain the minimum norm solution vectors.

8: $ldb$ – IntegerInput

On entry: the first dimension of the array b as declared in the (sub)program from which f08aaf (dgels) is called.

Constraint:

ldb \geq \max (1, m, n)

9: $work (\max (1, lwork))$ – Real (Kind=nag_wp) arrayWorkspace

On exit: if

info = 0

work (1)

contains the minimum value of lwork required for optimal performance.

10: $lwork$ – IntegerInput

On entry: the dimension of the array work as declared in the (sub)program from which f08aaf (dgels) is called.

lwork = - 1

, a workspace query is assumed; the routine only calculates the optimal size of the work array, returns this value as the first entry of the work array, and no error message related to lwork is issued.

Suggested value: for optimal performance,

lwork \geq \min (m, n) + \max (1, m, n, nrhs) \times nb

, where

nb

is the optimal block size.

Constraint:

lwork \geq \min (m, n) + \max (1, m, n, nrhs)

lwork = - 1

11: $info$ – IntegerOutput

On exit:

info = 0

unless the routine detects an error (see Section 6).

6

Error Indicators and Warnings

$info < 0$: If $info = - i$ , argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.

$info > 0$: If $info = i$ , diagonal element $i$ of the triangular factor of $A$ is zero, so that $A$ does not have full rank; the least squares solution could not be computed.

7

Accuracy

See Section 4.5 of Anderson et al. (1999) for details of error bounds.

8

Parallelism and Performance

f08aaf (dgels) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f08aaf (dgels) 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 factorize

A

is approximately

\frac{2}{3} n^{2} (3 m - n)

m \geq n

and

\frac{2}{3} m^{2} (3 n - m)

otherwise. Following the factorization the solution for a single vector

x

requires

O (\min (m^{2}, n^{2}))

operations.

The complex analogue of this routine is f08anf (zgels).

10

Example

This example solves the linear least squares problem

\min_{x} {‖b - A x‖}_{2},

where

A = (\begin{array}{r} - 0.57 & - 1.28 & - 0.39 & 0.25 \\ - 1.93 & 1.08 & - 0.31 & - 2.14 \\ 2.30 & 0.24 & 0.40 & - 0.35 \\ - 1.93 & 0.64 & - 0.66 & 0.08 \\ 0.15 & 0.30 & 0.15 & - 2.13 \\ - 0.02 & 1.03 & - 1.43 & 0.50 \end{array}) and b = (\begin{array}{r} - 2.67 \\ - 0.55 \\ 3.34 \\ - 0.77 \\ 0.48 \\ 4.10 \end{array}) .

The square root of the residual sum of squares is also output.

Note that the block size (NB) of

64

assumed in this example is not realistic for such a small problem, but should be suitable for large problems.

NAG Library Routine Document

f08aaf (dgels)

▸▿ 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