https://github.com/stdlib-js/blas-base-wasm-scnrm2

Multiply a vector `x` by a scalar `alpha`.
https://github.com/stdlib-js/blas-base-wasm-scnrm2

algebra array blas float float32 float32array javascript level-1 linear math mathematics ndarray node node-js nodejs scnrm2 stdlib subroutines vector webassembly

Last synced: 27 days ago
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Multiply a vector `x` by a scalar `alpha`.

Host: GitHub
URL: https://github.com/stdlib-js/blas-base-wasm-scnrm2
Owner: stdlib-js
License: apache-2.0
Created: 2025-06-22T12:11:05.000Z (5 months ago)
Default Branch: main
Last Pushed: 2025-07-16T04:57:54.000Z (4 months ago)
Last Synced: 2025-09-05T10:48:42.140Z (2 months ago)
Topics: algebra, array, blas, float, float32, float32array, javascript, level-1, linear, math, mathematics, ndarray, node, node-js, nodejs, scnrm2, stdlib, subroutines, vector, webassembly
Language: JavaScript
Homepage: https://github.com/stdlib-js/stdlib
Size: 863 KB
Stars: 0
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
- Notice: NOTICE

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# scnrm2

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] 

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

## Installation

```bash

npm install @stdlib/blas-base-wasm-scnrm2

```

Alternatively,

-   To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).

-   If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).

-   For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).

The [branches.md][branches-url] 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

```javascript

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

```

#### scnrm2.main( N, x, strideX )

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

```javascript

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

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

var z = scnrm2.main( 3, x, 1 );

// returns ~9.54

```

The function has the following parameters:

-   **N**: number of indexed elements.

-   **x**: input [`Complex64Array`][@stdlib/array/complex64].

-   **strideX**: index increment for `x`.

The `N` and stride parameters determine which elements in the input strided array are accessed at runtime. For example, to compute the L2-norm of every other element in `x`,

```javascript

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

var x = new Complex64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );

var z = scnrm2.main( 2, x, 2 );

// returns ~4.24

```

Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.

```javascript

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

// Initial array:

var x0 = new Complex64Array( [ 2.0, 1.0, 2.0, 2.0, -2.0, -2.0, 2.0, 3.0 ] );

// Create a typed array view:

var x1 = new Complex64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var z = scnrm2.main( 2, x1, 2 );

// returns ~4.58

```

#### scnrm2.ndarray( N, x, strideX, offsetX )

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

```javascript

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

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

var z = scnrm2.ndarray( 3, x, 1, 0 );

// returns ~9.54

```

The function has the following additional parameters:

-   **offsetX**: starting index for `x`.

While [`typed array`][mdn-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 L2-norm for every other value in `x` starting from the second value,

```javascript

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

var x = new Complex64Array( [ 2.0, 1.0, 2.0, 2.0, -2.0, -2.0, 2.0, 3.0 ] );

var z = scnrm2.ndarray( 2, x, 2, 1 );

// returns ~4.58

```

* * *

### Module

#### scnrm2.Module( memory )

Returns a new WebAssembly [module wrapper][@stdlib/wasm/module-wrapper] instance which uses the provided WebAssembly [memory][@stdlib/wasm/memory] instance as its underlying memory.

```javascript

var Memory = require( '@stdlib/wasm-memory' );

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):

var mem = new Memory({

    'initial': 10,

    'maximum': 100

});

// Create a BLAS routine:

var mod = new scnrm2.Module( mem );

// returns 

// Initialize the routine:

mod.initializeSync();

```

#### scnrm2.Module.prototype.main( N, xp, sx )

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

```javascript

var Memory = require( '@stdlib/wasm-memory' );

var oneTo = require( '@stdlib/array-one-to' );

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

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):

var mem = new Memory({

    'initial': 10,

    'maximum': 100

});

// Create a BLAS routine:

var mod = new scnrm2.Module( mem );

// returns 

// Initialize the routine:

mod.initializeSync();

// Define a vector data type:

var dtype = 'complex64';

// Specify a vector length:

var N = 5;

// Define a pointer (i.e., byte offset) for storing the input vector:

var xptr = 0;

// Write vector values to module memory:

var xbuf = oneTo( N*2, 'float32' );

var x = new Complex64Array( xbuf.buffer );

mod.write( xptr, x );

// Perform computation:

var out = mod.main( N, xptr, 1 );

// returns ~19.62

```

The function has the following parameters:

-   **N**: number of indexed elements.

-   **xp**: input [`Complex64Array`][@stdlib/array/complex64] pointer (i.e., byte offset).

-   **sx**: index increment for `x`.

#### scnrm2.Module.prototype.ndarray( N, xp, sx, ox )

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

```javascript

var Memory = require( '@stdlib/wasm-memory' );

var oneTo = require( '@stdlib/array-one-to' );

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

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):

var mem = new Memory({

    'initial': 10,

    'maximum': 100

});

// Create a BLAS routine:

var mod = new scnrm2.Module( mem );

// returns 

// Initialize the routine:

mod.initializeSync();

// Define a vector data type:

var dtype = 'complex64';

// Specify a vector length:

var N = 5;

// Define a pointer (i.e., byte offset) for storing the input vector:

var xptr = 0;

// Write vector values to module memory:

var xbuf = oneTo( N*2, 'float32' );

var x = new Complex64Array( xbuf.buffer );

mod.write( xptr, x );

// Perform computation:

var out = mod.ndarray( N, xptr, 1, 0 );

// returns ~19.62

```

The function has the following additional parameters:

-   **ox**: starting index for `x`.

* * *

## Notes

-   If `N <= 0`, both `main` and `ndarray` methods return `0.0`.

-   This package implements routines using WebAssembly. When provided arrays which are not allocated on a `scnrm2` module memory instance, data must be explicitly copied to module memory prior to computation. Data movement may entail a performance cost, and, thus, if you are using arrays external to module memory, you should prefer using [`@stdlib/blas-base/scnrm2`][@stdlib/blas/base/scnrm2]. However, if working with arrays which are allocated and explicitly managed on module memory, you can achieve better performance when compared to the pure JavaScript implementations found in [`@stdlib/blas/base/scnrm2`][@stdlib/blas/base/scnrm2]. Beware that such performance gains may come at the cost of additional complexity when having to perform manual memory management. Choosing between implementations depends heavily on the particular needs and constraints of your application, with no one choice universally better than the other.

-   `scnrm2()` corresponds to the [BLAS][blas] level 1 function [`scnrm2`][scnrm2].

* * *

## Examples

```javascript

var oneTo = require( '@stdlib/array-one-to' );

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

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

function main() {

    var xbuf;

    var out;

    var x;

    var N;

    // Specify a vector length:

    N = 5;

    // Create an input array:

    xbuf = oneTo( N*2, 'float32' );

    x = new Complex64Array( xbuf.buffer );

    // Perform computation:

    out = scnrm2.ndarray( N, x, 1, 0 );

    // Print the result:

    console.log( out );

}

```

* * *

## Notice

This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

## License

See [LICENSE][stdlib-license].

## Copyright

Copyright © 2016-2025. The Stdlib [Authors][stdlib-authors].

[npm-image]: http://img.shields.io/npm/v/@stdlib/blas-base-wasm-scnrm2.svg

[npm-url]: https://npmjs.org/package/@stdlib/blas-base-wasm-scnrm2

[test-image]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/actions/workflows/test.yml/badge.svg?branch=main

[test-url]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/actions/workflows/test.yml?query=branch:main

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/blas-base-wasm-scnrm2/main.svg

[coverage-url]: https://codecov.io/github/stdlib-js/blas-base-wasm-scnrm2?branch=main

[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg

[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd

[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/tree/deno

[deno-readme]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/blob/deno/README.md

[umd-url]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/tree/umd

[umd-readme]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/blob/umd/README.md

[esm-url]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/tree/esm

[esm-readme]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/blob/esm/README.md

[branches-url]: https://github.com/stdlib-js/blas-base-wasm-scnrm2/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/blas-base-wasm-scnrm2/main/LICENSE

[blas]: http://www.netlib.org/blas

[scnrm2]: https://www.netlib.org/lapack/explore-html/d1/d2a/group__nrm2_gab5393665c8f0e7d5de9bd1dd2ff0d9d0.html#gab5393665c8f0e7d5de9bd1dd2ff0d9d0

[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

[@stdlib/array/complex64]: https://github.com/stdlib-js/array-complex64

[@stdlib/wasm/memory]: https://github.com/stdlib-js/wasm-memory

[@stdlib/wasm/module-wrapper]: https://github.com/stdlib-js/wasm-module-wrapper

[@stdlib/blas/base/scnrm2]: https://github.com/stdlib-js/blas-base-scnrm2

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