sapxsumkbn

Add a scalar constant to each single-precision floating-point strided array element and compute the sum using an improved Kahan–Babuška algorithm.

Installation

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

Usage

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

sapxsumkbn( N, alpha, x, strideX )

Adds a scalar constant to each single-precision floating-point strided array element and computes the sum using an improved Kahan–Babuška algorithm.

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );

var v = sapxsumkbn( x.length, 5.0, x, 1 );
// returns 16.0

The function has the following parameters:

• N: number of indexed elements.
• alpha: scalar constant.
• x: input Float32Array.
• strideX: stride length.

The N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to access every other element in the strided array:

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );

var v = sapxsumkbn( 4, 5.0, x, 2 );
// returns 25.0

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

var Float32Array = require( '@stdlib/array-float32' );

var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var v = sapxsumkbn( 4, 5.0, x1, 2 );
// returns 25.0

sapxsumkbn.ndarray( N, alpha, x, strideX, offsetX )

Adds a scalar constant to each single-precision floating-point strided array element and computes the sum using an improved Kahan–Babuška algorithm and alternative indexing semantics.

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );

var v = sapxsumkbn.ndarray( x.length, 5.0, x, 1, 0 );
// returns 16.0

The function has the following additional parameters:

• offsetX: 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 access every other element starting from the second element:

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var v = sapxsumkbn.ndarray( 4, 5.0, x, 2, 1 );
// returns 25.0

Notes

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

Examples

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

var x = discreteUniform( 10, -100, 100, {
    'dtype': 'float32'
});
console.log( x );

var v = sapxsumkbn( x.length, 5.0, x, 1 );
console.log( v );

C APIs

Usage

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

stdlib_strided_sapxsumkbn( N, alpha, *X, strideX )

Adds a scalar constant to each single-precision floating-point strided array element and computes the sum using an improved Kahan–Babuška algorithm.

const float x[] = { 1.0f, 2.0f, 3.0f, 4.0f };

float v = stdlib_strided_sapxsumkbn( 4, 5.0f, x, 1 );
// returns 30.0f

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• alpha: [in] float scalar constant.
• X: [in] float* input array.
• strideX: [in] CBLAS_INT stride length.

float stdlib_strided_sapxsumkbn( const CBLAS_INT N, const float alpha, const float *X, const CBLAS_INT strideX );

stdlib_strided_sapxsumkbn_ndarray( N, alpha, *X, strideX, offsetX )

Adds a scalar constant to each single-precision floating-point strided array element and computes the sum using an improved Kahan–Babuška algorithm and alternative indexing semantics.

const float x[] = { 1.0f, 2.0f, 3.0f, 4.0f };

float v = stdlib_strided_sapxsumkbn_ndarray( 4, 5.0f, x, 1, 0 );
// returns 30.0f

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• alpha: [in] float scalar constant.
• X: [in] float* input array.
• strideX: [in] CBLAS_INT stride length.
• offsetX: [in] CBLAS_INT starting index.

float stdlib_strided_sapxsumkbn_ndarray( const CBLAS_INT N, const float alpha, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );

Examples

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

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

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

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

    // Compute the sum:
    float v = stdlib_strided_sapxsumkbn( N, 5.0f, x, strideX );

    // Print the result:
    printf( "Sum: %f\n", v );
}

References

• Neumaier, Arnold. 1974. "Rounding Error Analysis of Some Methods for Summing Finite Sums." Zeitschrift Für Angewandte Mathematik Und Mechanik 54 (1): 39–51. doi:10.1002/zamm.19740540106.

See Also

• @stdlib/blas-ext/base/dapxsumkbn: add a constant to each double-precision floating-point strided array element and compute the sum using an improved Kahan–Babuška algorithm.
• @stdlib/blas-ext/base/gapxsumkbn: add a scalar constant to each strided array element and compute the sum using an improved Kahan–Babuška algorithm.
• @stdlib/blas-ext/base/sapxsum: add a constant to each single-precision floating-point strided array element and compute the sum.
• @stdlib/blas-ext/base/ssumkbn: calculate the sum of single-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.

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.