drrss

Calculate the square root of the residual sum of squares of two double-precision floating-point strided arrays.

The square root of the residual sum of squares is defined as

Installation

npm install @stdlib/blas-ext-base-drrss

Usage

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

drrss( N, x, strideX, y, strideY )

Computes the square root of the residual sum of squares of two double-precision floating-point strided arrays.

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

var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var y = new Float64Array( [ 1.0, 1.0, -4.0 ] );

var z = drrss( x.length, x, 1, y, 1 );
// returns ~6.7

The function has the following parameters:

• N: number of indexed elements.
• x: first input Float64Array.
• strideX: stride length for x.
• y: second input Float64Array.
• strideY: stride length for y.

The N and stride parameters determine which elements in strided arrays are accessed at runtime. For example, to compute the residual sum of squares of every other element in x and y

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

var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var y = new Float64Array( [ 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 ] );

var z = drrss( x.length, x, 1, y, 1 );
// returns ~8.485

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

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

var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var y0 = new Float64Array( [ 8.0, -2.0, 3.0, -2.0, 7.0, -2.0, 0.0, -1.0 ] );

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*1 ); // start at 2nd element

var z = drrss( 4, x1, 2, y1, 2 );
// returns ~7.071

If N is less than or equal to 0, the function returns 0.

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

Computes the square root of the residual sum of squares of two double-precision floating-point strided arrays using alternative indexing semantics.

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

var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var y = new Float64Array( [ 1.0, 1.0, -4.0 ] );

var z = drrss.ndarray( x.length, x, 1, 0, y, 1, 0 );
// returns ~6.7

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 calculate the square root of the residual sum of squares for every other element in x and y starting from the second element

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

var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, 6.0 ] );
var y = new Float64Array( [ 8.0, -2.0, 3.0, -2.0, 7.0, -2.0, 0.0, -1.0, 4.0 ] );

var z = drrss.ndarray( 4, x, 2, 1, y, 2, 1 );
// returns ~7.071

Notes

• If N <= 0, both functions return 0.0.

Examples

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

var opts = {
    'dtype': 'float64'
};
var x = discreteUniform( 10, -50, 50, opts );
console.log( x );

var y = discreteUniform( 10, -50, 50, opts );
console.log( y );

var d = drrss( x.length, x, 1, y, 1 );
console.log( d );

C APIs

Usage

#include "stdlib/blas/ext/base/drrss.h"

stdlib_strided_drrss( N, *X, strideX, *Y, strideY )

Computes the square root of the residual sum of squares of two double-precision floating-point strided arrays.

const double x[] = { 1.0, -2.0, 2.0 };
const double y[] = { 1.0, 1.0, -4.0 };

double z = stdlib_strided_drrss( 3, x, 1, y, 1 );
// returns ~6.7

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] double* first input array.
• strideX: [in] CBLAS_INT stride length for X.
• Y: [in] double* second input array.
• strideY: [in] CBLAS_INT stride length for Y.

double stdlib_strided_drrss( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const double *Y, const CBLAS_INT strideY );

stdlib_strided_drrss_ndarray( N, *X, strideX, offsetX, *Y, strideY, offsetY )

Computes the square root of the residual sum of squares of two double-precision floating-point strided arrays using alternative indexing semantics.

const double x[] = { 1.0, -2.0, 2.0 };
const double y[] = { 1.0, 1.0, -4.0 };

double v = stdlib_strided_drrss_ndarray( 3, x, 1, 0, 1, 0 );
// returns ~6.7

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• X: [in] double* first input array.
• strideX: [in] CBLAS_INT stride length for X.
• offsetX: [in] CBLAS_INT starting index for X.
• Y: [in] double* second input array.
• strideY: [in] CBLAS_INT stride length for Y.
• offsetY: [in] CBLAS_INT starting index for Y.

double stdlib_strided_drrss_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, const double *Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );

Examples

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

int main( void ) {
    // Create two strided arrays:
    const double x[] = { 1.0, -2.0, -4.0, 5.0, 0.0, 3.0 };
    const double y[] = { 5.0, 12.0, -8.0, 15.0, 9.0, 0.0 };

    // Specify the number of elements:
    const int N = 5;

    // Specify the stride lengths:
    const int strideX = 1;
    const int strideY = 1;

    // Compute the square root of the residual sum of squares of `x` and `y`:
    double d = stdlib_strided_drrss( N, x, strideX, y, strideY );

    // Print the result:
    printf( "rrss: %lf\n", d );

    // Specify index offsets:
    const int offsetX = 1;
    const int offsetY = 1;

    // Compute the square root of the residual sum of squares of `x` and `y` with offsets:
    d = stdlib_strided_drrss_ndarray( N, x, strideX, offsetX, y, strideY, offsetY );

    // Print the result:
    printf( "rrss: %lf\n", d );
}

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 © 2016-2026. The Stdlib Authors.