npm package: @stdlib/blas-base-wasm-daxpy
Description: Multiply a vector `x` by a constant and add the result to `y`.
Author: The Stdlib Authors
Latest version: 0.1.1

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# daxpy

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

> Multiply a vector `x` by a constant `alpha` and add the result to `y`.

<section class="installation">

## Installation

```bash
npm install @stdlib/blas-base-wasm-daxpy
```

</section>

<section class="usage">

## Usage

```javascript
var daxpy = require( '@stdlib/blas-base-wasm-daxpy' );
```

#### daxpy.main( N, alpha, x, strideX, y, strideY )

Multiplies a vector `x` by a constant `alpha` and adds the result to `y`.

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

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

daxpy.main( x.length, 5.0, x, 1, y, 1 );
// y => <Float64Array>[ 6.0, 11.0, 16.0, 21.0, 26.0 ]
```

The function has the following parameters:

-   **N**: number of indexed elements.
-   **alpha**: scalar constant.
-   **x**: input [`Float64Array`][@stdlib/array/float64].
-   **strideX**: index increment for `x`.
-   **y**: input [`Float64Array`][@stdlib/array/float64].
-   **strideY**: index increment for `y`.

The `N` and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to multiply every other value in `x` by `alpha` and add the result to the first `N` elements of `y` in reverse order,

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

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

var alpha = 5.0;

daxpy.main( 3, alpha, x, 2, y, -1 );
// y => <Float64Array>[ 26.0, 16.0, 6.0, 1.0, 1.0, 1.0 ]
```

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

<!-- eslint-disable stdlib/capitalized-comments -->

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

// Initial arrays...
var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y0 = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element

daxpy.main( 3, 5.0, x1, -2, y1, 1 );
// y0 => <Float64Array>[ 7.0, 8.0, 9.0, 40.0, 31.0, 22.0 ]
```

#### daxpy.ndarray( N, alpha, x, strideX, offsetX, y, strideY, offsetY )

Multiplies a vector `x` by a constant `alpha` and adds the result to `y` using alternative indexing semantics.

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

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

daxpy.ndarray( x.length, alpha, x, 1, 0, y, 1, 0 );
// y => <Float64Array>[ 6.0, 11.0, 16.0, 21.0, 26.0 ]
```

The function has the following additional parameters:

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

While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to multiply every other value in `x` by a constant `alpha` starting from the second value and add to the last `N` elements in `y` where `x[i] -> y[n]`, `x[i+2] -> y[n-1]`,...,

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

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

var alpha = 5.0;

daxpy.ndarray( 3, alpha, x, 2, 1, y, -1, y.length-1 );
// y => <Float64Array>[ 7.0, 8.0, 9.0, 40.0, 31.0, 22.0 ]
```

* * *

### Module

#### daxpy.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.

<!-- eslint-disable node/no-sync -->

```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 daxpy.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();
```

#### daxpy.Module.prototype.main( N, α, xp, sx, yp, sy )

Multiplies a vector `x` by a constant and adds the result to `y`.

<!-- eslint-disable node/no-sync -->

```javascript
var Memory = require( '@stdlib/wasm-memory' );
var oneTo = require( '@stdlib/array-one-to' );
var ones = require( '@stdlib/array-ones' );
var zeros = require( '@stdlib/array-zeros' );
var bytesPerElement = require( '@stdlib/ndarray-base-bytes-per-element' );

// 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 daxpy.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();

// Define a vector data type:
var dtype = 'float64';

// Specify a vector length:
var N = 5;

// Define pointers (i.e., byte offsets) for storing two vectors:
var xptr = 0;
var yptr = N * bytesPerElement( dtype );

// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );
mod.write( yptr, ones( N, dtype ) );

// Perform computation:
mod.main( N, 5.0, xptr, 1, yptr, 1 );

// Read out the results:
var view = zeros( N, dtype );
mod.read( yptr, view );

console.log( view );
// => <Float64Array>[ 6.0, 11.0, 16.0, 21.0, 26.0 ]
```

The function has the following parameters:

-   **N**: number of indexed elements.
-   **α**: scalar constant.
-   **xp**: input [`Float64Array`][@stdlib/array/float64] pointer (i.e., byte offset).
-   **sx**: index increment for `x`.
-   **yp**: input [`Float64Array`][@stdlib/array/float64] pointer (i.e., byte offset).
-   **sy**: index increment for `y`.

#### daxpy.Module.prototype.ndarray( N, α, xp, sx, ox, yp, sy, oy )

Multiplies a vector `x` by a constant and adds the result to `y` using alternative indexing semantics.

<!-- eslint-disable node/no-sync -->

```javascript
var Memory = require( '@stdlib/wasm-memory' );
var oneTo = require( '@stdlib/array-one-to' );
var ones = require( '@stdlib/array-ones' );
var zeros = require( '@stdlib/array-zeros' );
var bytesPerElement = require( '@stdlib/ndarray-base-bytes-per-element' );

// 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 daxpy.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();

// Define a vector data type:
var dtype = 'float64';

// Specify a vector length:
var N = 5;

// Define pointers (i.e., byte offsets) for storing two vectors:
var xptr = 0;
var yptr = N * bytesPerElement( dtype );

// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );
mod.write( yptr, ones( N, dtype ) );

// Perform computation:
mod.ndarray( N, 5.0, xptr, 1, 0, yptr, 1, 0 );

// Read out the results:
var view = zeros( N, dtype );
mod.read( yptr, view );

console.log( view );
// => <Float64Array>[ 6.0, 11.0, 16.0, 21.0, 26.0 ]
```

The function has the following additional parameters:

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

</section>

<!-- /.usage -->

<section class="notes">

* * *

## Notes

-   If `N <= 0` or `alpha == 0`, `y` is left unchanged.
-   This package implements routines using WebAssembly. When provided arrays which are not allocated on a `daxpy` 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/daxpy`][@stdlib/blas/base/daxpy]. 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/daxpy`][@stdlib/blas/base/daxpy]. 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.
-   `daxpy()` corresponds to the [BLAS][blas] level 1 function [`daxpy`][daxpy].

</section>

<!-- /.notes -->

<section class="examples">

* * *

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var daxpy = require( '@stdlib/blas-base-wasm-daxpy' );

var opts = {
    'dtype': 'float64'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );

var y = discreteUniform( x.length, 0, 10, opts );
console.log( y );

daxpy.ndarray( x.length, 5.0, x, 1, 0, y, -1, y.length-1 );
console.log( y );
```

</section>

<!-- /.examples -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

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<section class="main-repo" >

* * *

## 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 &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

</section>

<!-- /.stdlib -->

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

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

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

[daxpy]: http://www.netlib.org/lapack/explore-html/de/da4/group__double__blas__level1.html

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

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