README
saxpy
Multiply a vector
x
by a constantalpha
and add the result toy
.
Installation
npm install @stdlib/blas-base-saxpy
Usage
var saxpy = require( '@stdlib/blas-base-saxpy' );
saxpy( N, alpha, x, strideX, y, strideY )
Multiplies a vector x
by a constant alpha
and adds the result to y
.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var y = new Float32Array( [ 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var alpha = 5.0;
saxpy( x.length, alpha, x, 1, y, 1 );
// y => <Float32Array>[ 6.0, 11.0, 16.0, 21.0, 26.0 ]
The function has the following parameters:
- N: number of indexed elements.
- alpha:
numeric
constant. - x: input
Float32Array
. - strideX: index increment for
x
. - y: input
Float32Array
. - strideY: index increment for
y
.
The N
and stride
parameters determine which elements in x
and y
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 Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float32Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var alpha = 5.0;
var N = floor( x.length / 2 );
saxpy( N, alpha, x, 2, y, -1 );
// y => <Float32Array>[ 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 Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );
// Initial arrays...
var x0 = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y0 = new Float32Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
// Create offset views...
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float32Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
var N = floor( x0.length / 2 );
saxpy( N, 5.0, x1, -2, y1, 1 );
// y0 => <Float32Array>[ 7.0, 8.0, 9.0, 40.0, 31.0, 22.0 ]
saxpy.ndarray( N, alpha, x, strideX, offsetX, y, strideY, offsetY )
Multiplies a vector x
by a constant alpha
and adds the result to y
using alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var y = new Float32Array( [ 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var alpha = 5.0;
saxpy.ndarray( x.length, alpha, x, 1, 0, y, 1, 0 );
// y => <Float32Array>[ 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 offsetX
and offsetY
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 Float32Array = require( '@stdlib/array-float32' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float32Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var alpha = 5.0;
var N = floor( x.length / 2 );
saxpy.ndarray( N, alpha, x, 2, 1, y, -1, y.length-1 );
// y => <Float32Array>[ 7.0, 8.0, 9.0, 40.0, 31.0, 22.0 ]
Notes
Examples
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float32Array = require( '@stdlib/array-float32' );
var saxpy = require( '@stdlib/blas-base-saxpy' );
var x;
var y;
var i;
x = new Float32Array( 10 );
y = new Float32Array( 10 );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = round( randu()*100.0 );
y[ i ] = round( randu()*10.0 );
}
console.log( x );
console.log( y );
saxpy.ndarray( x.length, 5.0, x, 1, 0, y, -1, y.length-1 );
console.log( y );
Notice
This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.
Community
License
See LICENSE.
Copyright
Copyright © 2016-2021. The Stdlib Authors.