F06ETF (DAXPYI) (PDF version)
F06 Chapter Contents
F06 Chapter Introduction
NAG Library Manual

NAG Library Routine Document

F06ETF (DAXPYI)

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.

 Contents

    1  Purpose
    7  Accuracy
    10  Example

1  Purpose

F06ETF (DAXPYI) adds a scaled sparse real vector, stored in compressed form, to an unscaled real vector.

2  Specification

SUBROUTINE F06ETF ( NZ, A, X, INDX, Y)
INTEGER  NZ, INDX(*)
REAL (KIND=nag_wp)  A, X(*), Y(*)
The routine may be called by its BLAS name daxpyi.

3  Description

F06ETF (DAXPYI) performs the operation
yαx+y  
where x is a sparse real vector, stored in compressed form, and y is a real vector in full storage form.

4  References

Dodson D S, Grimes R G and Lewis J G (1991) Sparse extensions to the Fortran basic linear algebra subprograms ACM Trans. Math. Software 17 253–263

5  Parameters

1:     NZ – INTEGERInput
On entry: the number of nonzeros in the sparse vector x.
2:     A – REAL (KIND=nag_wp)Input
On entry: the scalar α.
3:     X* – REAL (KIND=nag_wp) arrayInput
Note: the dimension of the array X must be at least max1,NZ .
On entry: the nonzero elements of the sparse vector x.
4:     INDX* – INTEGER arrayInput
Note: the dimension of the array INDX must be at least max1,NZ .
On entry: INDXi must contain the index of Xi in the sparse vector x, for i=1,2,,NZ.
Constraint: the indices must be distinct.
5:     Y* – REAL (KIND=nag_wp) arrayInput/Output
Note: the dimension of the array Y must be at least maxkINDXk .
On entry: the vector y. Only elements corresponding to indices in INDX are accessed.
On exit: the updated vector y.

6  Error Indicators and Warnings

None.

7  Accuracy

Not applicable.

8  Parallelism and Performance

Not applicable.

9  Further Comments

None.

10  Example

None.

F06ETF (DAXPYI) (PDF version)
F06 Chapter Contents
F06 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2015