Multiply a vector
xby a constantalphaand add the result toy.
var daxpy = require( '@stdlib/blas/base/daxpy' );Multiplies a vector x by a constant alpha and adds the result to y.
var Float64Array = require( '@stdlib/array/float64' );
var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var y = new Float64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var alpha = 5.0;
daxpy( x.length, alpha, x, 1, y, 1 );
// y => <Float64Array>[ 6.0, 11.0, 16.0, 21.0, 26.0 ]The function has the following parameters:
- N: number of indexed elements.
- alpha: scalar constant.
- x: input
Float64Array. - strideX: index increment for
x. - y: input
Float64Array. - strideY: index increment for
y.
The N and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to multiply every other value in x by alpha and add the result to the first N elements of y in reverse order,
var Float64Array = require( '@stdlib/array/float64' );
var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var alpha = 5.0;
daxpy( 3, alpha, x, 2, y, -1 );
// y => <Float64Array>[ 26.0, 16.0, 6.0, 1.0, 1.0, 1.0 ]Note that indexing is relative to the first index. To introduce an offset, use typed array views.
var Float64Array = require( '@stdlib/array/float64' );
// Initial arrays...
var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y0 = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
daxpy( 3, 5.0, x1, -2, y1, 1 );
// y0 => <Float64Array>[ 7.0, 8.0, 9.0, 40.0, 31.0, 22.0 ]Multiplies a vector x by a constant alpha and adds the result to y using alternative indexing semantics.
var Float64Array = require( '@stdlib/array/float64' );
var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var y = new Float64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var alpha = 5.0;
daxpy.ndarray( x.length, alpha, x, 1, 0, y, 1, 0 );
// y => <Float64Array>[ 6.0, 11.0, 16.0, 21.0, 26.0 ]The function has the following additional parameters:
- offsetX: starting index for
x. - offsetY: starting index for
y.
While typed array views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to multiply every other value in x by a constant alpha starting from the second value and add to the last N elements in y where x[i] -> y[n], x[i+2] -> y[n-1],...,
var Float64Array = require( '@stdlib/array/float64' );
var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var alpha = 5.0;
daxpy.ndarray( 3, alpha, x, 2, 1, y, -1, y.length-1 );
// y => <Float64Array>[ 7.0, 8.0, 9.0, 40.0, 31.0, 22.0 ]var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
var daxpy = require( '@stdlib/blas/base/daxpy' );
var opts = {
'dtype': 'float64'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );
var y = discreteUniform( x.length, 0, 10, opts );
console.log( y );
daxpy.ndarray( x.length, 5.0, x, 1, 0, y, -1, y.length-1 );
console.log( y );#include "stdlib/blas/base/daxpy.h"Multiplies a vector X by a constant and adds the result to Y.
const double x[] = { 1.0, 2.0, 3.0, 4.0 };
double y[] = { 0.0, 0.0, 0.0, 0.0 };
c_daxpy( 4, 5.0, x, 1, y, 1 );The function accepts the following arguments:
- N:
[in] CBLAS_INTnumber of indexed elements. - alpha:
[in] doublescalar constant. - X:
[in] double*input array. - strideX:
[in] CBLAS_INTindex increment forX. - Y:
[inout] double*output array. - strideY:
[in CBLAS_INTindex increment forY.
void c_daxpy( const CBLAS_INT N, const double alpha, const double *X, const CBLAS_INT strideX, double *Y, const CBLAS_INT strideY );Multiplies a vector X by a constant and adds the result to Y using alternative indexing semantics.
const double x[] = { 1.0, 2.0, 3.0, 4.0 };
double y[] = { 0.0, 0.0, 0.0, 0.0 };
c_daxpy_ndarray( 4, 5.0, x, 1, 0, y, 1, 0 );The function accepts the following arguments:
- N:
[in] CBLAS_INTnumber of indexed elements. - alpha:
[in] doublescalar constant. - X:
[in] double*input array. - strideX:
[in] CBLAS_INTindex increment forX. - offsetX:
[in] CBLAS_INTstarting index forX. - Y:
[inout] double*output array. - strideY:
[in CBLAS_INTindex increment forY. - offsetY:
[in] CBLAS_INTstarting index forY.
void c_daxpy_ndarray( const CBLAS_INT N, const double alpha, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, double *Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );#include "stdlib/blas/base/daxpy.h"
#include <stdio.h>
int main( void ) {
// Create strided arrays:
const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };
double y[] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
// Specify the number of elements:
const int N = 4;
// Specify stride lengths:
const int strideX = 2;
const int strideY = -2;
// Compute `a*x + y`:
c_daxpy( N, 5.0, x, strideX, y, strideY );
// Print the result:
for ( int i = 0; i < 8; i++ ) {
printf( "y[ %i ] = %lf\n", i, y[ i ] );
}
// Compute `a*x + y`:
c_daxpy_ndarray( N, 5.0, x, strideX, 1, y, strideY, 7 );
// Print the result:
for ( int i = 0; i < 8; i++ ) {
printf( "y[ %i ] = %lf\n", i, y[ i ] );
}
}@stdlib/blas/base/dasum: compute the sum of absolute values (L1 norm).@stdlib/blas/base/gaxpy: multiply x by a constant and add the result to y.@stdlib/blas/base/saxpy: multiply a vector x by a constant and add the result to y.