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    @stdlib/blas-base-daxpy
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    0.0.8 • Public • Published

    daxpy

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

    Installation

    npm install @stdlib/blas-base-daxpy

    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: numeric 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 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 Float64Array = require( '@stdlib/array-float64' );
    var floor = require( '@stdlib/math-base-special-floor' );
    
    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;
    var N = floor( x.length / 2 );
    
    daxpy( N, 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' );
    var floor = require( '@stdlib/math-base-special-floor' );
    
    // 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
    
    var N = floor( x0.length / 2 );
    
    daxpy( N, 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 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 Float64Array = require( '@stdlib/array-float64' );
    var floor = require( '@stdlib/math-base-special-floor' );
    
    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;
    var N = floor( x.length / 2 );
    
    daxpy.ndarray( N, 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-base-discrete-uniform' ).factory;
    var filledarrayBy = require( '@stdlib/array-filled-by' );
    var daxpy = require( '@stdlib/blas-base-daxpy' );
    
    var x = filledarrayBy( 10, 'float64', discreteUniform( 0, 100 ) );
    console.log( x );
    
    var y = filledarrayBy( x.length, 'float64', discreteUniform( 0, 10 ) );
    console.log( y );
    
    daxpy.ndarray( x.length, 5.0, x, 1, 0, y, -1, y.length-1 );
    console.log( y );

    See Also


    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.

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    License

    See LICENSE.

    Copyright

    Copyright © 2016-2022. The Stdlib Authors.

    Install

    npm i @stdlib/blas-base-daxpy

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    0.0.8

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