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dasumpw

Calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements using pairwise summation.

The L1 norm is defined as

$$\|\mathbf{x}\|_1 = \sum_{i=0}^{n-1} \vert x_i \vert$$

Installation

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

Alternatively,

To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
If you are using Deno, visit the deno branch (see README for usage intructions).
For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

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

dasumpw( N, x, strideX )

Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements using pairwise summation.

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

var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;

var v = dasumpw( N, x, 1 );
// returns 5.0

The function has the following parameters:

N: number of indexed elements.
x: input Float64Array.
strideX: index increment for x.

The N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of absolute values of every other element in x,

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 v = dasumpw( 4, x, 2 );
// returns 9.0

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

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

var 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 v = dasumpw( 4, x1, 2 );
// returns 9.0

dasumpw.ndarray( N, x, strideX, offsetX )

Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements using pairwise summation 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 = dasumpw.ndarray( N, x, 1, 0 );
// returns 5.0

The function has the following additional parameters:

offsetX: 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 absolute values of every other value in x starting from the second value

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 ] );

var v = dasumpw.ndarray( 4, x, 2, 1 );
// returns 9.0

Notes

If N <= 0, both functions return 0.0.
In general, pairwise summation is more numerically stable than ordinary recursive summation (i.e., "simple" summation), with slightly worse performance. While not the most numerically stable summation technique (e.g., compensated summation techniques such as the Kahan–Babuška-Neumaier algorithm are generally more numerically stable), pairwise summation strikes a reasonable balance between numerical stability and performance. If either numerical stability or performance is more desirable for your use case, consider alternative summation techniques.

Examples

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

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

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

C APIs

Usage

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

stdlib_strided_dasumpw( N, *X, strideX )

Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements using pairwise summation.

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

double v = stdlib_strided_dasumpw( 4, x, 1 );
// returns 10.0

The function accepts the following arguments:

N: [in] CBLAS_INT number of indexed elements.
X: [in] double* input array.
strideX: [in] CBLAS_INT index increment for X.

double stdlib_strided_dasumpw( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );

stdlib_strided_dasumpw_ndarray( N, *X, strideX, offsetX )

Computes the sum of absolute values (L1 norm) of double-precision floating-point strided array elements using pairwise summation and alternative indexing semantics.

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

double v = stdlib_strided_dasumpw_ndarray( 4, x, 1, 0 );
// returns 10.0

The function accepts the following arguments:

N: [in] CBLAS_INT number of indexed elements.
X: [in] double* input array.
strideX: [in] CBLAS_INT index increment for X.
offsetX: [in] CBLAS_INT starting index for X.

double stdlib_strided_dasumpw_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );

Examples

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

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

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

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

    // Compute the sum:
    double v = stdlib_strided_dasumpw( N, x, strideX );

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

References

Higham, Nicholas J. 1993. "The Accuracy of Floating Point Summation." SIAM Journal on Scientific Computing 14 (4): 783–99. doi:10.1137/0914050.

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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See LICENSE.

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README.md

README.md

dasumpw

Installation

Usage

dasumpw( N, x, strideX )

dasumpw.ndarray( N, x, strideX, offsetX )

Notes

Examples

C APIs

Usage

stdlib_strided_dasumpw( N, *X, strideX )

stdlib_strided_dasumpw_ndarray( N, *X, strideX, offsetX )

Examples

References

See Also

Notice

Community

License

Copyright

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README.md

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README.md

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dasumpw

Installation

Usage

dasumpw( N, x, strideX )

dasumpw.ndarray( N, x, strideX, offsetX )

Notes

Examples

C APIs

Usage

stdlib_strided_dasumpw( N, *X, strideX )

stdlib_strided_dasumpw_ndarray( N, *X, strideX, offsetX )

Examples

References

See Also

Notice

Community

License

Copyright