@stdlib/blas-base-dscal NPM

dscal

Multiply a double-precision floating-point vector x by a constant alpha.

Installation

npm install @stdlib/blas-base-dscal

Usage

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

dscal( N, alpha, x, stride )

Multiplies a double-precision floating-point vector x by a constant alpha.

var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

dscal( x.length, 5.0, x, 1 );
// x => <Float64Array>[ -10.0, 5.0, 15.0, -25.0, 20.0, 0.0, -5.0, -15.0 ]

The function has the following parameters:

N: number of indexed elements.
alpha: scalar constant.
x: input Float64Array.
stride: index increment.

The N and stride parameters determine which elements in x are accessed at runtime. For example, to multiply every other value by a constant

var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

dscal( 4, 5.0, x, 2 );
// x => <Float64Array>[ -10.0, 1.0, 15.0, -5.0, 20.0, 0.0, -5.0, -3.0 ]

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

var Float64Array = require( '@stdlib/array-float64' );

// Initial array...
var x0 = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

// Create an offset view...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

// Scale every other value...
dscal( 3, 5.0, x1, 2 );
// x0 => <Float64Array>[ 1.0, -10.0, 3.0, -20.0, 5.0, -30.0 ]

If either N or stride is less than or equal to 0, the function returns x unchanged.

dscal.ndarray( N, alpha, x, stride, offset )

Multiplies a double-precision floating-point vector x by a constant alpha using alternative indexing semantics.

var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

dscal.ndarray( x.length, 5.0, x, 1, 0 );
// x => <Float64Array>[ -10.0, 5.0, 15.0, -25.0, 20.0, 0.0, -5.0, -15.0 ]

The function has the following additional parameters:

offset: starting index.

While typed array views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to multiply the last three elements of x by a constant

var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

dscal.ndarray( 3, 5.0, x, 1, x.length-3 );
// x => <Float64Array>[ 1.0, -2.0, 3.0, -20.0, 25.0, -30.0 ]

Notes

If N <= 0, both functions return x unchanged.
dscal() corresponds to the BLAS level 1 function dscal.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var dscal = require( '@stdlib/blas-base-dscal' );

var opts = {
    'dtype': 'float64'
};
var x = discreteUniform( 10, -100, 100, opts );
console.log( x );

dscal( x.length, 5.0, x, 1 );
console.log( x );

C APIs

Usage

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

c_dscal( N, alpha, *X, stride )

Multiplies each element of a double-precision floating-point vector by a constant.

double x[] = { 1.0, 2.0, 3.0, 4.0 };

c_dscal( 4, 5.0, x, 1 );

The function accepts the following arguments:

N: [in] CBLAS_INT number of indexed elements.
alpha: [in] double scalar constant.
X: [inout] double* input array.
stride: [in] CBLAS_INT index increment for X.

void c_dscal( const CBLAS_INT N, const double alpha, double *X, const CBLAS_INT stride );

Examples

#include "stdlib/blas/base/dscal.h"
#include <stdio.h>

int main( void ) {
    // Create a strided array:
    double x[] = { 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0 };

    // Specify the number of elements:
    const int N = 8;

    // Specify a stride:
    const int strideX = 1;

    // Scale the vector:
    c_dscal( N, 5.0, x, strideX );

    // Print the result:
    for ( int i = 0; i < 8; i++ ) {
        printf( "x[ %i ] = %lf\n", i, x[ 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.