feat: add blas/base/ndarray/cgemv

lib/node_modules/@stdlib/blas/base/ndarray/cgemv/README.md

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+<!--
+
+@license Apache-2.0
+
+Copyright (c) 2026 The Stdlib Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+-->
+
+# cgemv
+
+> Perform one of the matrix-vector operations `y = alpha*A*x + beta*y`, `y = alpha*A^T*x + beta*y`, or `y = alpha*A^H*x + beta*y`.
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var cgemv = require( '@stdlib/blas/base/ndarray/cgemv' );
+```
+
+#### cgemv( arrays )
+
+Performs one of the matrix-vector operations `y = alpha*A*x + beta*y`, `y = alpha*A^T*x + beta*y`, or `y = alpha*A^H*x + beta*y`, where `alpha` and `beta` are scalars, `x` and `y` are one-dimensional ndarrays, and `A` is an `M` by `N` matrix.
+
+```javascript
+/* eslint-disable max-len */
+var Complex64Matrix = require( '@stdlib/ndarray/matrix/complex64' );
+var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
+var Complex64 = require( '@stdlib/complex/float32/ctor' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var str2enum = require( '@stdlib/blas/base/transpose-operation-str2enum' );
+
+var A = new Complex64Matrix( [ [ 1.0, 2.0, 3.0, 4.0 ], [ 5.0, 6.0, 7.0, 8.0 ] ] );
+var x = new Complex64Vector( [ 1.0, 2.0, 3.0, 4.0 ] );
+var y = new Complex64Vector( [ 1.0, 2.0, 3.0, 4.0 ] );
+
+var trans = scalar2ndarray( str2enum( 'no-transpose' ), {
+    'dtype': 'int8'
+});
+var alpha = scalar2ndarray( new Complex64( 1.0, 0.0 ), {
+    'dtype': 'complex64'
+});
+var beta = scalar2ndarray( new Complex64( 1.0, 0.0 ), {
+    'dtype': 'complex64'
+});
+
+var out = cgemv( [ A, x, y, trans, alpha, beta ] );
+// returns <ndarray>[ <Complex64>[ -9.0, 30.0 ], <Complex64>[ -15.0, 72.0 ] ]
+
+var bool = ( out === y );
+// returns true
+```
+
+The function has the following parameters:
+
+-   **arrays**: array-like object containing the following ndarrays:
+
+    -   a two-dimensional input ndarray corresponding to `A`.
+    -   a one-dimensional input ndarray corresponding to `x`.
+    -   a one-dimensional input/output ndarray corresponding to `y`.
+    -   a zero-dimensional ndarray specifying whether `A` should be transposed, conjugate-transposed, or not transposed corresponding to `trans`.
+    -   a zero-dimensional ndarray containing a scalar constant corresponding to `alpha`.
+    -   a zero-dimensional ndarray containing a scalar constant corresponding to `beta`.
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+/* eslint-disable max-len */
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
+var Complex64Matrix = require( '@stdlib/ndarray/matrix/complex64' );
+var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
+var Complex64 = require( '@stdlib/complex/float32/ctor' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var str2enum = require( '@stdlib/blas/base/transpose-operation-str2enum' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var cgemv = require( '@stdlib/blas/base/ndarray/cgemv' );
+
+var opts = {
+    'dtype': 'float32'
+};
+
+var A = new Complex64Matrix( discreteUniform( 24, 0, 10, opts ).buffer, 0, [ 3, 4 ] );
+var x = new Complex64Vector( discreteUniform( 8, 0, 10, opts ) );
+var y = new Complex64Vector( discreteUniform( 6, 0, 10, opts ) );
+
+var trans = scalar2ndarray( str2enum( 'no-transpose' ), {
+    'dtype': 'int8'
+});
+var alpha = scalar2ndarray( new Complex64( 1.0, 0.0 ), {
+    'dtype': 'complex64'
+});
+var beta = scalar2ndarray( new Complex64( 1.0, 0.0 ), {
+    'dtype': 'complex64'
+});
+
+var out = cgemv( [ A, x, y, trans, alpha, beta ] );
+console.log( ndarray2array( out ) );
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
+
+<section class="related">
+
+</section>
+
+<!-- /.related -->
+
+<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="links">
+
+</section>
+
+<!-- /.links -->

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

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+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var isnanf = require( '@stdlib/math/base/assert/is-nanf' );
+var realf = require( '@stdlib/complex/float32/real' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' );
+var str2enum = require( '@stdlib/blas/base/transpose-operation-str2enum' );
+var Complex64Vector = require( '@stdlib/ndarray/vector/complex64' );
+var Complex64Matrix = require( '@stdlib/ndarray/matrix/complex64' );
+var Complex64 = require( '@stdlib/complex/float32/ctor' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+var cgemv = require( './../lib' );
+
+
+// VARIABLES //
+
+var options = {
+	'dtype': 'complex64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+	var alpha;
+	var beta;
+	var trans;
+	var xbuf;
+	var ybuf;
+	var Abuf;
+	var A;
+	var x;
+	var y;
+
+	Abuf = uniform( len*len*2, -100.0, 100.0, {
+		'dtype': 'float32'
+	});
+	A = new Complex64Matrix( Abuf.buffer, 0, [ len, len ] );
+
+	xbuf = uniform( len*2, -100.0, 100.0, {
+		'dtype': 'float32'
+	});
+	x = new Complex64Vector( xbuf.buffer );
+
+	ybuf = uniform( len*2, -100.0, 100.0, {
+		'dtype': 'float32'
+	});
+	y = new Complex64Vector( ybuf.buffer );
+
+	alpha = scalar2ndarray( new Complex64( 1.0, 0.0 ), options );
+	beta = scalar2ndarray( new Complex64( 1.0, 0.0 ), options );
+	trans = scalar2ndarray( str2enum( 'no-transpose' ), {
+		'dtype': 'int8'
+	});
+
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var z;
+		var i;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			z = cgemv( [ A, x, y, trans, alpha, beta ] );
+			if ( typeof z !== 'object' ) {
+				b.fail( 'should return an ndarray' );
+			}
+		}
+		b.toc();
+		if ( isnanf( realf( z.get( i%len ) ) ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var len;
+	var min;
+	var max;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 3; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		len = pow( 10, i );
+		f = createBenchmark( len );
+		bench( format( '%s:len=%d', pkg, len ), f );
+	}
+}
+
+main();

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

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+
+{{alias}}( arrays )
+    Performs one of the matrix-vector operations `y = alpha*A*x + beta*y`,
+    `y = alpha*A^T*x + beta*y`, or `y = alpha*A^H*x + beta*y`, where `alpha`
+    and `beta` are scalars, `x` and `y` are one-dimensional ndarrays, and `A`
+    is an `M` by `N` matrix.
+
+    Parameters
+    ----------
+    arrays: ArrayLikeObject<ndarray>
+        Array-like object containing the following ndarrays:
+
+        - a two-dimensional input ndarray corresponding to `A`.
+        - a one-dimensional input ndarray corresponding to `x`.
+        - a one-dimensional input/output ndarray corresponding to `y`.
+        - a zero-dimensional ndarray Specifies whether `A` should be transposed, conjugate-transposed, or not transposed.
+        - a zero-dimensional ndarray containing a scalar constant corresponding to `alpha`.
+        - a zero-dimensional ndarray containing a scalar constant corresponding to `beta`.
+
+    Returns
+    -------
+    out: ndarray
+        Output ndarray.
+
+    Examples
+    --------
+    > var A = new {{alias:@stdlib/ndarray/matrix/complex64}}( [ [ 1.0, 2.0, 3.0, 4.0 ], [ 5.0, 6.0, 7.0, 8.0 ] ] );
+    > var x = new {{alias:@stdlib/ndarray/vector/complex64}}( [ 1.0, 2.0, 3.0, 4.0 ] );
+    > var y = new {{alias:@stdlib/ndarray/vector/complex64}}( [ 1.0, 2.0, 3.0, 4.0 ] );
+    > var ca = new {{alias:@stdlib/complex/float32/ctor}}( 1.0, 0.0 );
+    > var alpha = {{alias:@stdlib/ndarray/from-scalar}}( ca, { 'dtype': 'complex64' });
+    > var cb = new {{alias:@stdlib/complex/float32/ctor}}( 1.0, 0.0 );
+    > var beta = {{alias:@stdlib/ndarray/from-scalar}}( cb, { 'dtype': 'complex64' });
+    > var t = {{alias:@stdlib/blas/base/transpose-operation-str2enum}}( 'no-transpose' );
+    > var trans = {{alias:@stdlib/ndarray/from-scalar}}( t, { 'dtype': 'int8' });
+
+    > {{alias}}( [ A, x, y, trans, alpha, beta ] );
+    > y
+    <ndarray>[ <Complex64>[ -9.0, 30.0 ], <Complex64>[ -15.0, 72.0 ] ]
+
+    See Also
+    --------
+