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feat: add blas/base/ndarray/sspmv
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‎lib/node_modules/@stdlib/blas/base/ndarray/sspmv/README.md‎

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<!--
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@license Apache-2.0
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Copyright (c) 2026 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# sspmv
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> Perform the matrix-vector operation `y = alpha*A*x + beta*y` for a symmetric matrix supplied in packed form `A`.
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<section class="intro">
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var sspmv = require( '@stdlib/blas/base/ndarray/sspmv' );
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```
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#### sspmv( arrays )
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Performs the matrix-vector operation `y = alpha*A*x + beta*y` for a symmetric matrix supplied in packed form `A`, where `alpha` and `beta` are scalars, `x` and `y` are ndarrays, and `A` is an `N` by `N` matrix.
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```javascript
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var Float32Vector = require( '@stdlib/ndarray/vector/float32' );
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var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
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var ndarray = require( '@stdlib/ndarray/base/ctor' );
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var Float32Array = require( '@stdlib/array/float32' );
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var A = new ndarray( 'float32', new Float32Array( [ 1.0, 2.0, 3.0, 1.0, 2.0, 1.0 ] ), [ 6 ], [ 1 ], 0, 'row-major' );
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var x = new Float32Vector( [ 1.0, 2.0, 3.0 ] );
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var y = new Float32Vector( [ 4.0, 5.0, 6.0 ] );
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var alpha = scalar2ndarray( 3.0, {
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'dtype': 'float32'
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});
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var beta = scalar2ndarray( 2.0, {
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'dtype': 'float32'
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});
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var out = sspmv( [ A, x, y, alpha, beta ] );
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// returns <ndarray>[ 50.0, 40.0, 42.0 ]
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var bool = ( out === y );
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// returns true
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```
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The function has the following parameters:
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- **arrays**: array-like object containing the following ndarrays:
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- a one-dimensional symmetric input ndarray.
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- first one-dimensional input ndarray.
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- second one-dimensional input ndarray.
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- first zero-dimensional ndarray containing a scalar constant.
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- second zero-dimensional ndarray containing a scalar constant.
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</section>
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<!-- /.usage -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var ndarray = require( '@stdlib/ndarray/base/ctor' );
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var Float32Array = require( '@stdlib/array/float32' );
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var Float32Vector = require( '@stdlib/ndarray/vector/float32' );
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var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var sspmv = require( '@stdlib/blas/base/ndarray/sspmv' );
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var opts = {
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'dtype': 'float32'
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};
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var A = new ndarray( 'float32', new Float32Array( discreteUniform( 10, 0, 10, opts ) ), [ 10 ], [ 1 ], 0, 'row-major' );
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var x = new Float32Vector( discreteUniform( 4, 0, 10, opts ) );
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var y = new Float32Vector( discreteUniform( 4, 0, 10, opts ) );
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var alpha = scalar2ndarray( 3.0, opts );
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var beta = scalar2ndarray( 2.0, opts );
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var out = sspmv( [ A, x, y, alpha, beta ] );
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console.log( ndarray2array( out ) );
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```
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</section>
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<!-- /.examples -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/blas/base/ndarray/sspmv/benchmark/benchmark.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/uniform' );
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var isnanf = require( '@stdlib/math/base/assert/is-nanf' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
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var format = require( '@stdlib/string/format' );
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var pkg = require( './../package.json' ).name;
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var sspmv = require( './../lib' );
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// VARIABLES //
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var options = {
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'dtype': 'float32'
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};
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// FUNCTIONS //
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/**
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* Creates a benchmark function.
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*
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* @private
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* @param {PositiveInteger} len - array length
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var alpha;
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var beta;
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var x;
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var y;
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var A;
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A = uniform( [ (len * (len + 1)) / 2 ], -100.0, 100.0, options );
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x = uniform( [ len ], -100.0, 100.0, options );
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y = uniform( [ len ], -100.0, 100.0, options );
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alpha = scalar2ndarray( 1.0, options );
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beta = scalar2ndarray( 1.0, options );
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return benchmark;
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/**
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* Benchmark function.
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*
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* @private
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* @param {Benchmark} b - benchmark instance
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*/
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function benchmark( b ) {
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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z = sspmv( [ A, x, y, alpha, beta ] );
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if ( typeof z !== 'object' ) {
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b.fail( 'should return an ndarray' );
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}
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}
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b.toc();
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if ( isnanf( z.get( i%len ) ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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/**
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* Main execution sequence.
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*
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* @private
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*/
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function main() {
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var len;
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var min;
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var max;
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var f;
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var i;
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min = 1; // 10^min
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max = 3; // 10^max
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for ( i = min; i <= max; i++ ) {
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len = pow( 10, i );
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f = createBenchmark( len );
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bench( format( '%s:len=%d', pkg, len ), f );
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}
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}
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main();

‎lib/node_modules/@stdlib/blas/base/ndarray/sspmv/docs/repl.txt‎

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{{alias}}( arrays )
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Performs the matrix-vector operation `y = alpha*A*x + beta*y` for a
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symmetric matrix supplied in packed form `A`, where `alpha` and `beta`
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are scalars, `x` and `y` are ndarrays, and `A` is an `N` by `N` matrix.
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Parameters
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----------
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arrays: ArrayLikeObject<ndarray>
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Array-like object containing the following ndarrays:
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- a one-dimensional symmetric input ndarray.
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- first one-dimensional input ndarray.
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- second one-dimensional input ndarray.
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- first zero-dimensional ndarray containing a scalar constant.
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- second zero-dimensional ndarray containing a scalar constant.
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Returns
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-------
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out: ndarray
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Output ndarray.
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Examples
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--------
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> var buf = new {{alias:@stdlib/array/float32}}( [ 2.0, 1.0, 2.0 ] );
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> var sh = [ 3 ];
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> var st = [ 1 ];
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> var A = new {{alias:@stdlib/ndarray/base/ctor}}( 'float32', buf, sh, st, 0, 'row-major' );
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> var x = new {{alias:@stdlib/ndarray/vector/float32}}( [ 1.0, 2.0 ] );
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> var y = new {{alias:@stdlib/ndarray/vector/float32}}( [ 3.0, 4.0 ] );
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> var alpha = {{alias:@stdlib/ndarray/from-scalar}}( 2.0, { 'dtype': 'float32' });
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> var beta = {{alias:@stdlib/ndarray/from-scalar}}( 3.0, { 'dtype': 'float32' });
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> {{alias}}( [ A, x, y, alpha, beta ] );
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> y
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<ndarray>[ 17.0, 22.0 ]
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See Also
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--------
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‎lib/node_modules/@stdlib/blas/base/ndarray/sspmv/docs/types/index.d.ts‎

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/*
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* @license Apache-2.0
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*
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* Copyright (c) 2026 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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// TypeScript Version: 4.1
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/// <reference types="@stdlib/types"/>
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import { float32ndarray } from '@stdlib/types/ndarray';
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/**
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* Performs the matrix-vector operation `y = alpha*A*x + beta*y` for a symmetric matrix supplied in packed form `A`, where `alpha` and `beta` are scalars, `x` and `y` are ndarrays, and `A` is an `N` by `N` matrix.
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*
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* ## Notes
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*
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* - The function expects the following ndarrays:
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*
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* - first one-dimensional symmetric input ndarray.
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* - second one-dimensional input ndarray.
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* - third one-dimensional input ndarray.
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* - first zero-dimensional ndarray containing a scalar constant.
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* - second zero-dimensional ndarray containing a scalar constant.
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*
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* @param arrays - array-like object containing ndarrays
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* @returns output ndarray
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*
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* @example
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* var Float32Vector = require( '@stdlib/ndarray/vector/float32' );
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* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
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* var ndarray = require( '@stdlib/ndarray/base/ctor' );
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* var Float32Array = require( '@stdlib/array/float32' );
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*
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* var A = new ndarray( 'float32', new Float32Array( [ 1.0, 2.0, 3.0, 1.0, 2.0, 1.0 ] ), [ 6 ], [ 1 ], 0, 'row-major' );
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* var x = new Float32Vector( [ 1.0, 2.0, 3.0 ] );
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* var y = new Float32Vector( [ 4.0, 5.0, 6.0 ] );
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*
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* var alpha = scalar2ndarray( 3.0, {
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* 'dtype': 'float32'
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* });
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* var beta = scalar2ndarray( 2.0, {
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* 'dtype': 'float32'
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* });
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*
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* var out = sspmv( [ A, x, y, alpha, beta ] );
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* // returns <ndarray>[ 50.0, 40.0, 42.0 ]
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*
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* var bool = ( out === y );
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* // returns true
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*/
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declare function sspmv( arrays: [ float32ndarray, float32ndarray, float32ndarray, float32ndarray, float32ndarray ] ): float32ndarray;
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// EXPORTS //
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export = sspmv;

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