@stdlib/blas-base-scnrm2 v0.1.0

scnrm2

Compute the L2-norm of a complex single-precision floating-point vector.

Installation

npm install @stdlib/blas-base-scnrm2

Usage

var scnrm2 = require( '@stdlib/blas-base-scnrm2' );

scnrm2( N, cx, strideX )

Computes the L2-norm of a complex single-precision floating-point vector.

var Complex64Array = require( '@stdlib/array-complex64' );

var cx = new Complex64Array( [ 0.3, 0.1, 0.5, 0.0, 0.0, 0.5, 0.0, 0.2 ] );

var norm = scnrm2( 4, cx, 1 );
// returns ~0.8

The function has the following parameters:

• N: number of indexed elements.
• cx: input Complex64Array.
• strideX: index increment for cx.

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

var Complex64Array = require( '@stdlib/array-complex64' );

var cx = new Complex64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var norm = scnrm2( 2, cx, 2 );
// returns ~4.6

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

var Complex64Array = require( '@stdlib/array-complex64' );

// Initial array:
var cx0 = new Complex64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

// Create an offset view:
var cx1 = new Complex64Array( cx0.buffer, cx0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

// Compute the L2-norm:
var norm = scnrm2( 2, cx1, 1 );
// returns ~9.3

scnrm2.ndarray( N, cx, strideX, offset )

Computes the L2-norm of a complex single-precision floating-point vector using alternative indexing semantics.

var Complex64Array = require( '@stdlib/array-complex64' );

var cx = new Complex64Array( [ 0.3, 0.1, 0.5, 0.0, 0.0, 0.5, 0.0, 0.2 ] );

var norm = scnrm2.ndarray( 4, cx, 1, 0 );
// returns ~0.8

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 start from the second index,

var Complex64Array = require( '@stdlib/array-complex64' );

var cx = new Complex64Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

var norm = scnrm2.ndarray( 2, cx, 1, 1 );
// returns ~9.3

Notes

• If N <= 0, both functions return 0.0.
• scnrm2() corresponds to the BLAS level 1 function scnrm2.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var Complex64 = require( '@stdlib/complex-float32-ctor' );
var scnrm2 = require( '@stdlib/blas-base-scnrm2' );

function rand() {
    return new Complex64( discreteUniform( 0, 10 ), discreteUniform( -5, 5 ) );
}

var cx = filledarrayBy( 10, 'complex64', rand );
console.log( cx.toString() );

// Compute the L2-norm:
var norm = scnrm2( cx.length, cx, 1 );
console.log( norm );

C APIs

Usage

#include "stdlib/blas/base/scnrm2.h"

c_scnrm2( N, *CX, strideX )

Computes the L2-norm of a complex single-precision floating-point vector.

const float cx[] = { 0.3f, 0.1f, 0.5f, 0.0f, 0.0f, 0.5f, 0.0f, 0.2f };

float norm = c_scnrm2( 4, (void *)cx, 1 );
// returns 0.8

The function accepts the following arguments:

• N: [in] CBLAS_INT number of indexed elements.
• CX: [in] void* input array.
• strideX: [in] CBLAS_INT index increment for CX.

float c_scnrm2( const CBLAS_INT N, const void *CX, const CBLAS_INT strideX );

Examples

#include "stdlib/blas/base/scnrm2.h"
#include <stdio.h>

int main( void ) {
    // Create a strided array of interleaved real and imaginary components:
    const float cx[] = { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f };

    // Specify the number of elements:
    const int N = 4;

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

    // Compute the L2-norm:
    c_scnrm2( N, (void *)cx, strideX );

    // Print the result:
    printf( "L2-norm: %f\n", norm );
}

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 © 2016-2024. The Stdlib Authors.