About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

daxpy

Multiply a vector x by a constant alpha and add the result to y.

Installation

npm install @stdlib/blas-base-daxpy

Alternatively,

To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
If you are using Deno, visit the deno branch (see README for usage intructions).
For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var daxpy = require( '@stdlib/blas-base-daxpy' );

daxpy( N, alpha, x, strideX, y, strideY )

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 ]

daxpy.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 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 ]

Notes

If N <= 0 or alpha == 0, both functions return y unchanged.
daxpy() corresponds to the BLAS level 1 function daxpy.

Examples

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 );

C APIs

Usage

#include "stdlib/blas/base/daxpy.h"

c_daxpy( N, alpha, X, strideX, Y, strideY )

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_INT number of indexed elements.
alpha: [in] double scalar constant.
X: [in] double* input array.
strideX: [in] CBLAS_INT index increment for X.
Y: [inout] double* output array.
strideY: [in CBLAS_INT index increment for Y.

void c_daxpy( const CBLAS_INT N, const double alpha, const double *X, const CBLAS_INT strideX, double *Y, const CBLAS_INT strideY );

c_daxpy_ndarray( N, alpha, X, strideX, offsetX, Y, strideY, offsetY )

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_INT number of indexed elements.
alpha: [in] double scalar constant.
X: [in] double* input array.
strideX: [in] CBLAS_INT index increment for X.
offsetX: [in] CBLAS_INT starting index for X.
Y: [inout] double* output array.
strideY: [in CBLAS_INT index increment for Y.
offsetY: [in] CBLAS_INT starting index for Y.

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 );

Examples

#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 ] );
    }
}

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.

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

README.md

README.md

daxpy

Installation

Usage

daxpy( N, alpha, x, strideX, y, strideY )

daxpy.ndarray( N, alpha, x, strideX, offsetX, y, strideY, offsetY )

Notes

Examples

C APIs

Usage

c_daxpy( N, alpha, X, strideX, Y, strideY )

c_daxpy_ndarray( N, alpha, X, strideX, offsetX, Y, strideY, offsetY )

Examples

See Also

Notice

Community

License

Copyright

Files

README.md

Latest commit

History

README.md

File metadata and controls

daxpy

Installation

Usage

daxpy( N, alpha, x, strideX, y, strideY )

daxpy.ndarray( N, alpha, x, strideX, offsetX, y, strideY, offsetY )

Notes

Examples

C APIs

Usage

c_daxpy( N, alpha, *X, strideX, *Y, strideY )

c_daxpy_ndarray( N, alpha, *X, strideX, offsetX, *Y, strideY, offsetY )

Examples

See Also

Notice

Community

License

Copyright

c_daxpy( N, alpha, X, strideX, Y, strideY )

c_daxpy_ndarray( N, alpha, X, strideX, offsetX, Y, strideY, offsetY )