@stdlib/blas-base-dasum

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dasum

Compute the sum of absolute values (L1 norm).

The L1 norm is defined as

Installation

npm install @stdlib/blas-base-dasum

Usage

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

dasum( N, x, stride )

Computes the sum of absolute values.

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 ] );

var sum = dasum( x.length, x, 1 );
// returns 19.0

The function has the following parameters:

• N: number of indexed elements.
• x: input Float64Array.
• stride: index increment.

The N and stride parameters determine which elements in x are used to compute the sum. For example, to sum every other value,

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 ] );

var sum = dasum( 4, x, 2 );
// returns 10.0

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

// Sum every other value...
var sum = dasum( 3, x1, 2 );
// returns 12.0

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

dasum.ndarray( N, x, stride, offset )

Computes the sum of absolute values 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 ] );

var sum = dasum.ndarray( x.length, x, 1, 0 );
// returns 19.0

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 sum the last three elements,

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

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

var sum = dasum.ndarray( 3, x, 1, x.length-3 );
// returns 15.0

// Using a negative stride to sum from the last element:
sum = dasum.ndarray( 3, x, -1, x.length-1 );
// returns 15.0

Notes

• If N <= 0, the sum is 0.
• dasum() corresponds to the BLAS level 1 function dasum.

Examples

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

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

var out = dasum( x.length, x, 1 );
console.log( out );

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See Also

• @stdlib/blas-base/daxpy: multiply a vector x by a constant and add the result to y.
• @stdlib/blas-base/gasum: compute the sum of absolute values (L1 norm).
• @stdlib/blas-base/sasum: compute the sum of absolute values (L1 norm).
• @stdlib/blas-ext/base/dsum: calculate the sum of double-precision floating-point strided array elements.

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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

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.