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feat: add examples, benchmarks, ts files
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‎lib/node_modules/@stdlib/lapack/base/dorg2r/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) 2025 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/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var floor = require( '@stdlib/math/base/special/floor' );
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var pkg = require( './../package.json' ).name;
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var dorg2r = require( './../lib/dorg2r.js' );
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// VARIABLES //
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var LAYOUTS = [
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'row-major',
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'column-major'
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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 {string} order - storage layout
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* @param {PositiveInteger} N - matrix order (N-by-N)
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* @returns {Function} benchmark function
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*/
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function createBenchmark( order, N ) {
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var work;
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var opts;
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var tau;
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var A;
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opts = {
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'dtype': 'float64'
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};
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// Random input matrix and auxiliary arrays:
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A = uniform( N*N, -1.0, 1.0, opts );
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tau = uniform( N, 0.1, 1.0, opts );
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work = uniform( N, 0.0, 0.0, opts );
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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 = dorg2r( order, N, N, 0, A, N, tau, work );
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if ( isnan( z[ i%z.length ] ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z[ i%z.length ] ) ) {
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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 min;
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var max;
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var ord;
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var N;
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var f;
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var i;
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var k;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( k = 0; k < LAYOUTS.length; k++ ) {
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ord = LAYOUTS[ k ];
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for ( i = min; i <= max; i++ ) {
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N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
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f = createBenchmark( ord, N );
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bench( pkg+':order='+ord+',size='+(N*N), f );
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}
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}
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}
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main();

‎lib/node_modules/@stdlib/lapack/base/dorg2r/benchmark/benchmark.ndarray.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 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/array/uniform' );
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var isColumnMajor = require( '@stdlib/ndarray/base/assert/is-column-major-string' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var floor = require( '@stdlib/math/base/special/floor' );
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var pkg = require( './../package.json' ).name;
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var dorg2r = require( './../lib/ndarray.js' );
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// VARIABLES //
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var LAYOUTS = [
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'row-major',
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'column-major'
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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 {string} order - storage layout
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* @param {PositiveInteger} N - matrix order (N-by-N)
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* @returns {Function} benchmark function
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*/
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function createBenchmark( order, N ) {
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var work;
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var sa1;
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var sa2;
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var tau;
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var A;
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if ( isColumnMajor( order ) ) {
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sa1 = 1;
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sa2 = N;
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} else {
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sa1 = N;
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sa2 = 1;
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}
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A = uniform( N*N, -1.0, 1.0, {
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'dtype': 'float64'
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});
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tau = uniform( N, 0.1, 1.0, {
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'dtype': 'float64'
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});
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work = uniform( N, 0.0, 0.0, {
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'dtype': 'float64'
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});
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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 = dorg2r( N, N, 0, A, sa1, sa2, 0, tau, 1, 0, work, 1, 0 );
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if ( isnan( z[ i%z.length ] ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z[ i%z.length ] ) ) {
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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 min;
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var max;
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var ord;
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var N;
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var f;
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var i;
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var k;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( k = 0; k < LAYOUTS.length; k++ ) {
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ord = LAYOUTS[ k ];
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for ( i = min; i <= max; i++ ) {
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N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
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f = createBenchmark( ord, N );
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bench( pkg+':ndarray:order='+ord+',size='+(N*N), f );
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}
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}
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}
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main();

‎lib/node_modules/@stdlib/lapack/base/dorg2r/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) 2025 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 { Layout } from '@stdlib/types/blas';
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/**
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* Interface describing `dorg2r`.
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*/
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interface Routine {
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/**
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* Generates an `M`-by-`N` real matrix `Q` with orthonormal columns. The matrix `Q` is defined as the first `N` columns of a product of `K` elementary reflectors of order `M`. `Q = H(1) H(2) . . . H(K)` as returned by `dgeqrf`.
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*
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*
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* @param order - storage layout
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* @param M - number of rows in `A`
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* @param N - number of columns in `A`
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* @param K - number of elementary reflectors whose product defines the matrix `Q`
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* @param A - input matrix (overwritten with `Q` on output)
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* @param LDA - stride of the first dimension of `A` (leading dimension)
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* @param tau - vector of `K` scalar factors of the elementary reflectors
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* @param work - workspace array
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* @returns matrix `A` overwritten with the orthogonal matrix `Q`
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*
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* @example
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* var Float64Array = require( '@stdlib/array/float64' );
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* var dorg2r = require( '@stdlib/lapack/base/dorg2r' );
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*
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* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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* var tau = new Float64Array( [ 0.0, 0.0 ] );
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* var work = new Float64Array( 10 );
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*
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* dorg2r( 'column-major', 3, 2, 2, A, 3, tau, work );
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* // A => <Float64Array>[ 1.0, 0.0, 0.0, 0.0, 1.0, 0.0 ]
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*/
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( order: Layout, M: number, N: number, K: number, A: Float64Array, LDA: number, tau: Float64Array, work: Float64Array ): Float64Array;
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/**
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* Generates an `M`-by-`N` real matrix `Q` with orthonormal columns using alternative indexing semantics. The matrix `Q` is defined as the first `N` columns of a product of `K` elementary reflectors of order `M`. `Q = H(1) H(2) . . . H(K)` as returned by `dgeqrf`.
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*
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* @param M - number of rows in `A`
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* @param N - number of columns in `A`
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* @param K - number of elementary reflectors
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* @param A - input matrix
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* @param strideA1 - stride of the first dimension of `A`
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* @param strideA2 - stride of the second dimension of `A`
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* @param offsetA - index offset for `A`
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* @param tau - vector of scalar factors of the elementary reflectors
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* @param strideTau - stride length for `tau`
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* @param offsetTau - index offset for `tau`
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* @param work - workspace array
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* @param strideWork - stride length for `work`
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* @param offsetWork - index offset for `work`
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* @returns matrix `A` overwritten with the orthogonal matrix `Q`
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*
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* @example
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* var Float64Array = require( '@stdlib/array/float64' );
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* var dorg2r = require( '@stdlib/lapack/base/dorg2r' );
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*
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* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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* var tau = new Float64Array( [ 0.0, 0.0 ] );
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* var work = new Float64Array( 10 );
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*
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* dorg2r.ndarray( 3, 2, 2, A, 1, 3, 0, tau, 1, 0, work, 1, 0 );
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* // A => <Float64Array>[ 1.0, 0.0, 0.0, 0.0, 1.0, 0.0 ]
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*/
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ndarray( M: number, N: number, K: number, A: Float64Array, strideA1: number, strideA2: number, offsetA: number, tau: Float64Array, strideTau: number, offsetTau: number, work: Float64Array, strideWork: number, offsetWork: number ): Float64Array; // eslint-disable-line max-len
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}
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/**
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* Generates an `M`-by-`N` real matrix `Q` with orthonormal columns. The matrix `Q` is defined as the first `N` columns of a product of `K` elementary reflectors of order `M`. `Q = H(1) H(2) . . . H(K)` as returned by `dgeqrf`.
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*
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* @param order - storage layout
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* @param M - number of rows in `A`
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* @param N - number of columns in `A`
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* @param K - number of elementary reflectors whose product defines the matrix `Q`
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* @param A - input matrix (overwritten with `Q` on output)
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* @param LDA - stride of the first dimension of `A` (leading dimension)
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* @param tau - vector of `K` scalar factors of the elementary reflectors
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* @param work - workspace array
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* @returns matrix `A` overwritten with the orthogonal matrix `Q`
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*
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* @example
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* var Float64Array = require( '@stdlib/array/float64' );
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* var dorg2r = require( '@stdlib/lapack/base/dorg2r' );
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*
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* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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* var tau = new Float64Array( [ 0.0, 0.0 ] );
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* var work = new Float64Array( 10 );
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*
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* dorg2r( 'column-major', 3, 2, 2, A, 3, tau, work );
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* // A => <Float64Array>[ 1.0, 0.0, 0.0, 0.0, 1.0, 0.0 ]
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*
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* @example
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* var Float64Array = require( '@stdlib/array/float64' );
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* var dorg2r = require( '@stdlib/lapack/base/dorg2r' );
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*
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* var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
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* var tau = new Float64Array( [ 0.0, 0.0 ] );
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* var work = new Float64Array( 10 );
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*
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* dorg2r.ndarray( 3, 2, 2, A, 1, 3, 0, tau, 1, 0, work, 1, 0 );
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* // A => <Float64Array>[ 1.0, 0.0, 0.0, 0.0, 1.0, 0.0 ]
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*/
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declare var dorg2r: Routine;
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// EXPORTS //
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export = dorg2r;

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