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sgemv

Perform one of the matrix-vector operations y = α*A*x + β*y or y = α*A^T*x + β*y.

Installation

npm install @stdlib/blas-base-sgemv

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md 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

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

sgemv( order, trans, M, N, α, A, LDA, x, sx, β, y, sy )

Performs one of the matrix-vector operations y = α*A*x + β*y or y = α*A^T*x + β*y, where α and β are scalars, x and y are vectors, and A is an M by N matrix.

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

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

sgemv( 'row-major', 'no-transpose', 2, 3, 1.0, A, 3, x, 1, 1.0, y, 1 );
// y => <Float32Array>[ 7.0, 16.0 ]

The function has the following parameters:

• order: storage layout.
• trans: specifies whether A should be transposed, conjugate-transposed, or not transposed.
• M: number of rows in the matrix A.
• N: number of columns in the matrix 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 operations are performed. For example, to iterate over every other element in x and y,

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

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

sgemv( 'row-major', 'no-transpose', 2, 2, 1.0, A, 2, x, 2, 1.0, y, 2 );
// y => <Float32Array>[ 4.0, 0.0, 8.0, 0.0 ]

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 ] );
var y0 = new Float32Array( [ 0.0, 1.0, 1.0 ] );
var A = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );

// 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

sgemv( 'row-major', 'no-transpose', 2, 2, 1.0, A, 2, x1, -1, 1.0, y1, -1 );
// y0 => <Float32Array>[ 0.0, 8.0, 4.0 ]

sgemv.ndarray( trans, M, N, α, A, sa1, sa2, oa, x, sx, ox, β, y, sy, oy )

Performs one of the matrix-vector operations y = α*A*x + β*y or y = α*A^T*x + β*y, using alternative indexing semantics and where α and β are scalars, x and y are vectors, and A is an M by N matrix.

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

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

sgemv.ndarray( 'no-transpose', 2, 3, 1.0, A, 3, 1, 0, x, 1, 0, 1.0, y, 1, 0 );
// y => <Float32Array>[ 7.0, 16.0 ]

The function has the following additional parameters:

• sa1: stride of the first dimension of A.
• sa2: stride of 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, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var x = new Float32Array( [ 0.0, 1.0, 2.0, 3.0 ] );
var y = new Float32Array( [ 7.0, 8.0, 9.0, 10.0 ] );

sgemv.ndarray( 'no-transpose', 2, 3, 1.0, A, 3, 1, 0, x, 1, 1, 1.0, y, -2, 2 );
// y => <Float32Array>[ 39, 8, 23, 10 ]

Notes

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

Examples

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

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

var M = 3;
var N = 3;

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

sgemv( 'row-major', 'no-transpose', M, N, 1.0, A, N, x, -1, 1.0, y, -1 );
console.log( y );

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C APIs

Usage

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

c_sgemv( layout, trans, M, N, alpha, *A, LDA, *X, strideX, beta, *Y, strideY )

Performs one of the matrix-vector operations y = α*A*x + β*y or y = α*A^T*x + β*y, where α and β are scalars, x and y are vectors, and A is an M by N matrix.

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

const float A[] = { 1.0f, 0.0f, 0.0f, 2.0f, 1.0f, 0.0f, 3.0f, 2.0f, 1.0f };
const float x[] = { 1.0f, 2.0f, 3.0f };
float y[] = { 1.0f, 2.0f, 3.0f };

c_sgemv( CblasColMajor, CblasNoTrans, 3, 3, 1.0f, A, 3, x, 1, 1.0f, y, 1 );

The function accepts the following arguments:

• layout: [in] CBLAS_LAYOUT storage layout.
• trans: [in] CBLAS_TRANSPOSE specifies whether A should be transposed, conjugate-transposed, or not transposed.
• M: [in] CBLAS_INT number of rows in the matrix A.
• N: [in] CBLAS_INT number of columns in the matrix A.
• alpha: [in] float scalar constant.
• A: [in] float* input matrix.
• LDA: [in] CBLAS_INT stride of the first dimension of A (a.k.a., leading dimension of the matrix A).
• X: [in] float* first input vector.
• strideX: [in] CBLAS_INT stride length for X.
• beta: [in] float scalar constant.
• Y: [inout] float* second input vector.
• strideY: [in] CBLAS_INT stride length for Y.

void c_sgemv( const CBLAS_LAYOUT layout, const CBLAS_TRANSPOSE trans, const CBLAS_INT M, const CBLAS_INT N, const float alpha, const float *A, const CBLAS_INT LDA, const float *X, const CBLAS_INT strideX, const float beta, float *Y, const CBLAS_INT strideY )

c_sgemv_ndarray( trans, M, N, alpha, *A, sa1, sa2, oa, *X, sx, ox, beta, *Y, sy, oy )

Performs one of the matrix-vector operations y = α*A*x + β*y or y = α*A^T*x + β*y, using indexing alternative semantics and where α and β are scalars, x and y are vectors, and A is an M by N matrix.

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

const float A[] = { 1.0f, 0.0f, 0.0f, 2.0f, 1.0f, 0.0f, 3.0f, 2.0f, 1.0f };
const float x[] = { 1.0f, 2.0f, 3.0f };
float y[] = { 1.0f, 2.0f, 3.0f };

c_sgemv_ndarray( CblasNoTrans, 3, 3, 1.0f, A, 1, 3, 0, x, 1, 0, 1.0f, y, 1, 0 );

The function accepts the following arguments:

• trans: [in] CBLAS_TRANSPOSE specifies whether A should be transposed, conjugate-transposed, or not transposed.
• M: [in] CBLAS_INT number of rows in the matrix A.
• N: [in] CBLAS_INT number of columns in the matrix A.
• alpha: [in] float scalar constant.
• A: [in] float* input matrix.
• sa1: [in] CBLAS_INT stride of the first dimension of A.
• sa2: [in] CBLAS_INT stride of the second dimension of A.
• oa: [in] CBLAS_INT starting index for A.
• X: [in] float* first input vector.
• sx: [in] CBLAS_INT stride length for X.
• ox: [in] CBLAS_INT starting index for X.
• beta: [in] float scalar constant.
• Y: [inout] float* second input vector.
• sy: [in] CBLAS_INT stride length for Y.
• oy: [in] CBLAS_INT starting index for Y.

void c_sgemv_ndarray( const CBLAS_TRANSPOSE trans, const CBLAS_INT M, const CBLAS_INT N, const float alpha, const float *A, const CBLAS_INT strideA1, const CBLAS_INT strideA2, const CBLAS_INT offsetA, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, const float beta, float *Y, const CBLAS_INT strideY, const CBLAS_INT offsetY )

Examples

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

int main( void ) {
    // Define a 3x3 matrix stored in row-major order:
    const float A[ 3*3 ] = {
        1.0f, 2.0f, 3.0f,
        4.0f, 5.0f, 6.0f,
        7.0f, 8.0f, 9.0f
    };

    // Define `x` and `y` vectors:
    const float x[ 3 ] = { 1.0f, 2.0f, 3.0f };
    float y[ 3 ] = { 1.0f, 2.0f, 3.0f };

    // Specify the number of elements along each dimension of `A`:
    const int M = 3;
    const int N = 3;

    // Perform the matrix-vector operation `y = α*A*x + β*y`:
    c_sgemv( CblasRowMajor, CblasNoTrans, M, N, 1.0f, A, M, x, 1, 1.0f, y, 1 );

    // Print the result:
    for ( int i = 0; i < N; i++ ) {
        printf( "y[ %i ] = %f\n", i, y[ i ] );
    }

    // Perform the matrix-vector operation `y = α*A*x + β*y` using alternative indexing semantics:
    c_sgemv_ndarray( CblasNoTrans, M, N, 1.0f, A, N, 1, 0, x, 1, 0, 1.0f, y, 1, 0 );

    // Print the result:
    for ( int i = 0; i < N; i++ ) {
        printf( "y[ %i ] = %f\n", i, y[ i ] );
    }
}

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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

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License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.