ssymv

Perform the matrix-vector operation y = α*A*x + β*y.

Installation

npm install @stdlib/blas-base-ssymv

Usage

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

ssymv( order, uplo, N, α, A, LDA, x, sx, β, y, sy )

Performs the matrix-vector operation y = α*A*x + β*y where α and β are scalars, x and y are N element vectors, and A is an N by N symmetric matrix.

var Float32Array = require( '@stdlib/array-float32' );

var A = new Float32Array( [ 1.0, 4.0, 5.0, 4.0, 2.0, 6.0, 5.0, 6.0, 3.0 ] );
var x = new Float32Array( [ 1.0, 1.0, 1.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0 ] );

ssymv( 'row-major', 'lower', 3, 1.0, A, 3, x, 1, 0.0, y, 1 );
// y => <Float32Array>[ 10.0, 12.0, 14.0 ]

The function has the following parameters:

• order: storage layout.
• uplo: specifies whether the upper or lower triangular part of the symmetric matrix A should be referenced.
• N: number of elements along each dimension of A.
• α: scalar constant.
• A: input matrix stored in linear memory as a Float32Array.
• LDA: stride of the first dimension of A (a.k.a., leading dimension of the matrix A).
• x: input Float32Array.
• sx: stride length for x.
• β: scalar constant.
• y: output Float32Array.
• sy: stride length for y.

The stride parameters determine how elements in the input arrays are accessed at runtime. For example, to iterate over the elements of x in reverse order,

var Float32Array = require( '@stdlib/array-float32' );

var A = new Float32Array( [ 1.0, 4.0, 5.0, 4.0, 2.0, 6.0, 5.0, 6.0, 3.0 ] );
var x = new Float32Array( [ 1.0, 1.0, 1.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0 ] );

ssymv( 'row-major', 'upper', 3, 1.0, A, 3, x, -1, 1.0, y, 1 );
// y => <Float32Array>[ 10.0, 12.0, 14.0 ]

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

var Float32Array = require( '@stdlib/array-float32' );

// Initial arrays...
var x0 = new Float32Array( [ 0.0, 1.0, 1.0, 1.0 ] );
var y0 = new Float32Array( [ 0.0, 0.0, 0.0, 0.0 ] );
var A = new Float32Array( [ 1.0, 4.0, 5.0, 4.0, 2.0, 6.0, 5.0, 6.0, 3.0 ] );

// Create offset views...
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float32Array( y0.buffer, y0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

ssymv( 'row-major', 'upper', 3, 1.0, A, 3, x1, -1, 1.0, y1, -1 );
// y0 => <Float32Array>[ 0.0, 14.0, 12.0, 10.0 ]

ssymv.ndarray( uplo, N, α, A, sa1, sa2, oa, x, sx, ox, β, y, sy, oy )

Performs the matrix-vector operation y = α*A*x + β*y using alternative indexing semantics and where α and β are scalars, x and y are N element vectors, and A is an N by N symmetric matrix.

var Float32Array = require( '@stdlib/array-float32' );

var A = new Float32Array( [ 1.0, 4.0, 5.0, 4.0, 2.0, 6.0, 5.0, 6.0, 3.0 ] );
var x = new Float32Array( [ 1.0, 1.0, 1.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0 ] );

ssymv.ndarray( 'upper', 3, 1.0, A, 3, 1, 0, x, -1, 2, 1.0, y, 1, 0 );
// y => <Float32Array>[ 10.0, 12.0, 14.0 ]

The function has the following additional parameters:

• sa1: stride for the first dimension of A.
• sa2: stride for the second dimension of A.
• oa: starting index for A.
• ox: starting index for x.
• oy: starting index for y.

While typed array views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example,

var Float32Array = require( '@stdlib/array-float32' );

var A = new Float32Array( [ 1.0, 4.0, 5.0, 4.0, 2.0, 6.0, 5.0, 6.0, 3.0 ] );
var x = new Float32Array( [ 1.0, 1.0, 1.0 ] );
var y = new Float32Array( [ 0.0, 0.0, 0.0 ] );

ssymv.ndarray( 'lower', 3, 1.0, A, 3, 1, 0, x, -1, 2, 1.0, y, -1, 2 );
// y => <Float32Array>[ 14.0, 12.0, 10.0 ]

Notes

• ssymv() corresponds to the BLAS level 2 function ssymv.

Examples

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

var opts = {
    'dtype': 'float32'
};

var N = 5;
var A = ones( N*N, opts.dtype );

var x = discreteUniform( N, 0, 255, opts );
var y = discreteUniform( N, 0, 255, opts );

ssymv( 'row-major', 'upper', N, 1.0, A, N, x, 1, 1.0, y, 1 );
console.log( y );

ssymv.ndarray( 'upper', N, 1.0, A, N, 1, 0, x, 1, 0, 1.0, y, 1, 0 );
console.log( y );

C APIs

Usage

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Examples

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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

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.