NAG Library Function Document

nag_dtpmv (f16phc)

 Contents

    1  Purpose
    7  Accuracy

1
Purpose

nag_dtpmv (f16phc) performs matrix-vector multiplication for a real triangular matrix stored in packed form.

2
Specification

#include <nag.h>
#include <nagf16.h>
void  nag_dtpmv (Nag_OrderType order, Nag_UploType uplo, Nag_TransType trans, Nag_DiagType diag, Integer n, double alpha, const double ap[], double x[], Integer incx, NagError *fail)

3
Description

nag_dtpmv (f16phc) performs one of the matrix-vector operations
xαAx   or   xαATx ,  
where A is an n by n real triangular matrix, stored in packed form, x is an n-element real vector and α is a real scalar.

4
References

Basic Linear Algebra Subprograms Technical (BLAST) Forum (2001) Basic Linear Algebra Subprograms Technical (BLAST) Forum Standard University of Tennessee, Knoxville, Tennessee http://www.netlib.org/blas/blast-forum/blas-report.pdf

5
Arguments

1:     order Nag_OrderTypeInput
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by order=Nag_RowMajor. See Section 3.3.1.3 in How to Use the NAG Library and its Documentation for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2:     uplo Nag_UploTypeInput
On entry: specifies whether A is upper or lower triangular.
uplo=Nag_Upper
A is upper triangular.
uplo=Nag_Lower
A is lower triangular.
Constraint: uplo=Nag_Upper or Nag_Lower.
3:     trans Nag_TransTypeInput
On entry: specifies the operation to be performed.
trans=Nag_NoTrans
xαAx.
trans=Nag_Trans or Nag_ConjTrans
xαATx.
Constraint: trans=Nag_NoTrans, Nag_Trans or Nag_ConjTrans.
4:     diag Nag_DiagTypeInput
On entry: specifies whether A has nonunit or unit diagonal elements.
diag=Nag_NonUnitDiag
The diagonal elements are stored explicitly.
diag=Nag_UnitDiag
The diagonal elements are assumed to be 1 and are not referenced.
Constraint: diag=Nag_NonUnitDiag or Nag_UnitDiag.
5:     n IntegerInput
On entry: n, the order of the matrix A.
Constraint: n0.
6:     alpha doubleInput
On entry: the scalar α.
7:     ap[dim] const doubleInput
Note: the dimension, dim, of the array ap must be at least max1, n × n+1 / 2 .
On entry: the n by n triangular matrix A, packed by rows or columns.
The storage of elements Aij depends on the order and uplo arguments as follows:
  • if order=Nag_ColMajor and uplo=Nag_Upper,
              Aij is stored in ap[j-1×j/2+i-1], for ij;
  • if order=Nag_ColMajor and uplo=Nag_Lower,
              Aij is stored in ap[2n-j×j-1/2+i-1], for ij;
  • if order=Nag_RowMajor and uplo=Nag_Upper,
              Aij is stored in ap[2n-i×i-1/2+j-1], for ij;
  • if order=Nag_RowMajor and uplo=Nag_Lower,
              Aij is stored in ap[i-1×i/2+j-1], for ij.
8:     x[dim] doubleInput/Output
Note: the dimension, dim, of the array x must be at least max1,1+n-1incx.
On entry: the right-hand side vector b.
On exit: the solution vector x.
9:     incx IntegerInput
On entry: the increment in the subscripts of x between successive elements of x.
Constraint: incx0.
10:   fail NagError *Input/Output
The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

6
Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_INT
On entry, incx=value.
Constraint: incx0.
On entry, n=value.
Constraint: n0.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.

7
Accuracy

The BLAS standard requires accurate implementations which avoid unnecessary over/underflow (see Section 2.7 of Basic Linear Algebra Subprograms Technical (BLAST) Forum (2001)).

8
Parallelism and Performance

nag_dtpmv (f16phc) is not threaded in any implementation.

9
Further Comments

None.

10
Example

This example computes the matrix-vector product
y=αAx  
where
A = 1.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 3.0 3.0 3.0 0.0 4.0 4.0 4.0 4.0 ,  
x = 1.0 -2.0 3.0 -1.0  
and
α=1.5 .  

10.1
Program Text

Program Text (f16phce.c)

10.2
Program Data

Program Data (f16phce.d)

10.3
Program Results

Program Results (f16phce.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017