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blas-base-wasm-dnrm2

Calculate the L2-norm of a double-precision floating-point vector.

https://github.com/stdlib-js/blas-base-wasm-dnrm2

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2-norm algebra blas dnrm2 euclidean javascript l2-norm level-1 linear magnitude math mathematics node node-js nodejs norm nrm2 stdlib subroutines vector
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Calculate the L2-norm of a double-precision floating-point vector.

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2-norm algebra blas dnrm2 euclidean javascript l2-norm level-1 linear magnitude math mathematics node node-js nodejs norm nrm2 stdlib subroutines vector
Created over 1 year ago · Last pushed 7 months ago
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dnrm2

NPM version Build Status Coverage Status <!-- dependencies -->

Calculate the L2-norm of a double-precision floating-point vector.

## Installation ```bash npm install @stdlib/blas-base-wasm-dnrm2 ``` 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 dnrm2 = require( '@stdlib/blas-base-wasm-dnrm2' ); ``` #### dnrm2.main( N, x, strideX ) Calculates the L2-norm of a double-precision floating-point vector. ```javascript var Float64Array = require( '@stdlib/array-float64' ); var x = new Float64Array( [ 1.0, -2.0, 2.0 ] ); var z = dnrm2.main( 3, x, 1 ); // returns 3.0 ``` The function has the following parameters: - **N**: number of indexed elements. - **x**: input [`Float64Array`][@stdlib/array/float64]. - **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 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 z = dnrm2.main( 4, x, 2 ); // returns 5.0 ``` Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views. ```javascript var Float64Array = require( '@stdlib/array-float64' ); // Initial array: var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); // Create a typed array view: var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element var z = dnrm2.main( 4, x1, 2 ); // returns 5.0 ``` #### dnrm2.ndarray( N, x, strideX, offsetX ) Calculates the L2-norm of a double-precision floating-point vector using alternative indexing semantics. ```javascript var Float64Array = require( '@stdlib/array-float64' ); var x = new Float64Array( [ 1.0, -2.0, 2.0 ] ); var z = dnrm2.ndarray( 3, x, 1, 0 ); // returns 3.0 ``` 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 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 z = dnrm2.ndarray( 4, x, 2, 1 ); // returns 5.0 ``` * * * ### Module #### dnrm2.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 dnrm2.Module( mem ); // returns // Initialize the routine: mod.initializeSync(); ``` #### dnrm2.Module.prototype.main( N, xp, sx ) Computes the L2-norm of a double-precision floating-point vector. ```javascript var Memory = require( '@stdlib/wasm-memory' ); var oneTo = require( '@stdlib/array-one-to' ); // 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 dnrm2.Module( mem ); // returns // Initialize the routine: mod.initializeSync(); // Define a vector data type: var dtype = 'float64'; // 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: mod.write( xptr, oneTo( N, dtype ) ); // Perform computation: var out = mod.main( N, xptr, 1 ); // returns ~7.42 ``` The function has the following parameters: - **N**: number of indexed elements. - **xp**: input [`Float64Array`][@stdlib/array/float64] pointer (i.e., byte offset). - **sx**: index increment for `x`. #### dnrm2.Module.prototype.ndarray( N, xp, sx, ox ) Computes the L2-norm of a double-precision floating-point vector using alternative indexing semantics. ```javascript var Memory = require( '@stdlib/wasm-memory' ); var oneTo = require( '@stdlib/array-one-to' ); // 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 dnrm2.Module( mem ); // returns // Initialize the routine: mod.initializeSync(); // Define a vector data type: var dtype = 'float64'; // 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: mod.write( xptr, oneTo( N, dtype ) ); // Perform computation: var out = mod.ndarray( N, xptr, 1, 0 ); // returns ~7.42 ``` 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 `dnrm2` 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/dnrm2`][@stdlib/blas/base/dnrm2]. 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/dnrm2`][@stdlib/blas/base/dnrm2]. 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. - `dnrm2()` corresponds to the [BLAS][blas] level 1 function [`dnrm2`][dnrm2].
* * * ## Examples ```javascript var discreteUniform = require( '@stdlib/random-array-discrete-uniform' ); var dnrm2 = require( '@stdlib/blas-base-wasm-dnrm2' ); var opts = { 'dtype': 'float64' }; var x = discreteUniform( 10, 0, 100, opts ); console.log( x ); var out = dnrm2.ndarray( x.length, x, 1, 0 ); 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].

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Standard library for JavaScript.

Citation (CITATION.cff) 

cff-version: 1.2.0
title: stdlib
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  If you use this software, please cite it using the
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authors:
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repository-code: https://github.com/stdlib-js/stdlib
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abstract: |
  Standard library for JavaScript and Node.js.

keywords:
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  - standard library
  - scientific computing
  - numerical computing
  - statistical computing

license: Apache-2.0 AND BSL-1.0

date-released: 2016

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