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Calculate the sum of double-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.
npm install @stdlib/blas-ext-base-dsumkbn
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var dsumkbn = require( '@stdlib/blas-ext-base-dsumkbn' );
Computes the sum of double-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;
var v = dsumkbn( N, x, 1 );
// returns 1.0
The function has the following parameters:
- N: number of indexed elements.
- x: input
Float64Array
. - stride: index increment for
x
.
The N
and stride
parameters determine which elements in x
are accessed at runtime. For example, to compute the sum of every other element in x
,
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var N = floor( x.length / 2 );
var v = dsumkbn( N, x, 2 );
// returns 5.0
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' );
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var N = floor( x0.length / 2 );
var v = dsumkbn( N, x1, 2 );
// returns 5.0
Computes the sum of double-precision floating-point strided array elements using an improved Kahan–Babuška algorithm and alternative indexing semantics.
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;
var v = dsumkbn.ndarray( N, x, 1, 0 );
// returns 1.0
The function has the following additional parameters:
- offset: starting index for
x
.
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 calculate the sum of every other value in x
starting from the second value
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var N = floor( x.length / 2 );
var v = dsumkbn.ndarray( N, x, 2, 1 );
// returns 5.0
- If
N <= 0
, both functions return0.0
.
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var dsumkbn = require( '@stdlib/blas-ext-base-dsumkbn' );
var x;
var i;
x = new Float64Array( 10 );
for ( i = 0; i < x.length; i++ ) {
x[ i ] = round( randu()*100.0 );
}
console.log( x );
var v = dsumkbn( x.length, x, 1 );
console.log( v );
- 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.
@stdlib/blas-ext/base/dnansumkbn
: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using an improved Kahan–Babuška algorithm.@stdlib/blas-ext/base/dsum
: calculate the sum of double-precision floating-point strided array elements.@stdlib/blas-ext/base/dsumkbn2
: calculate the sum of double-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm.@stdlib/blas-ext/base/dsumors
: calculate the sum of double-precision floating-point strided array elements using ordinary recursive summation.@stdlib/blas-ext/base/dsumpw
: calculate the sum of double-precision floating-point strided array elements using pairwise summation.@stdlib/blas-ext/base/gsumkbn
: calculate the sum of strided array elements using an improved Kahan–Babuška algorithm.@stdlib/blas-ext/base/ssumkbn
: calculate the sum of single-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.
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.
See LICENSE.
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