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feat: add lapack/base/dlascl
#7515
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<!-- | ||||||
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@license Apache-2.0 | ||||||
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Copyright (c) 2025 The Stdlib Authors. | ||||||
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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 | ||||||
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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 | ||||||
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. | ||||||
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--> | ||||||
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# dlascl | ||||||
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> LAPACK routine to multiply a real M by N matrix `A` by a real scalar `CTO/CFROM`. | ||||||
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<section class="usage"> | ||||||
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## Usage | ||||||
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```javascript | ||||||
var dlascl = require( '@stdlib/lapack/base/dlascl' ); | ||||||
``` | ||||||
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#### dlascl( order, type, KL, KU, CFROM, CTO, M, N, A, LDA ) | ||||||
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Multiplies a real M by N matrix `A` by a real scalar `CTO/CFROM`. | ||||||
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```javascript | ||||||
var Float64Array = require( '@stdlib/array/float64' ); | ||||||
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var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] ); // => [ [ 1.0, 2.0 ], [ 3.0, 4.0 ], [ 5.0, 6.0 ] ] | ||||||
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dlascl( 'row-major', 'general', 0, 0, 1.0, 2.0, 3, 2, A, 2 ); | ||||||
// A => <Float64Array>[ 2.0, 4.0, 6.0, 8.0, 10.0, 12.0 ] | ||||||
``` | ||||||
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The function has the following parameters: | ||||||
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- **order**: storage layout. | ||||||
- **type**: specifies the type of matrix `A` ( should be one of these: `general`, `upper`, `lower`, `upper-hessenberg`, `symmetric-banded-lower`, `symmetric-banded-upper` or `banded` ). | ||||||
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- **KL**: lower band width of `A`. Referenced only if type is `symmetric-banded-lower` or `banded`. | ||||||
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- **KU**: upper band width of `A`. Referenced only if type is `symmetric-banded-upper` or `banded`. | ||||||
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- **CFROM**: the matrix `A` is multiplied by `CTO / CFROM`. | ||||||
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- **CTO**: the matrix `A` is multiplied by `CTO / CFROM`. | ||||||
- **M**: number of rows in matrix `A`. | ||||||
- **N**: number of columns in matrix `A`. | ||||||
- **A**: input [`Float64Array`][mdn-float64array]. | ||||||
- **LDA**: stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`). | ||||||
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If `type` is `banded`, `symmetric-banded-lower` or `symmetric-banded-upper` the matrix should be stored in the [`Band storage`][lapack-band-storage] format. | ||||||
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Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views. | ||||||
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<!-- eslint-disable stdlib/capitalized-comments --> | ||||||
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```javascript | ||||||
var Float64Array = require( '@stdlib/array/float64' ); | ||||||
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// Initial arrays... | ||||||
var A0 = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] ); | ||||||
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// Create offset views... | ||||||
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var A1 = new Float64Array( A0.buffer, A0.BYTES_PER_ELEMENT*1 ); // start at 2nd element | ||||||
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dlascl( 'row-major', 'general', 0, 0, 1.0, 2.0, 3, 2, A1, 2 ); | ||||||
// A0 => <Float64Array>[ 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0 ] | ||||||
``` | ||||||
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#### dlascl.ndarray( type, KL, KU, CFROM, CTO, M, N, A, strideA1, strideA2, offsetA ) | ||||||
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Multiplies a real M by N matrix `A` by a real scalar `CTO/CFROM` using alternative indexing semantics. | ||||||
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```javascript | ||||||
var Float64Array = require( '@stdlib/array/float64' ); | ||||||
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var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] ); // => [ [ 1.0, 2.0 ], [ 3.0, 4.0 ], [ 5.0, 6.0 ] ] | ||||||
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dlascl.ndarray( 'general', 0, 0, 1.0, 2.0, 3, 2, A, 2, 1, 0 ); | ||||||
// A => <Float64Array>[ 2.0, 4.0, 6.0, 8.0, 10.0, 12.0 ] | ||||||
``` | ||||||
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The function has the following additional parameters: | ||||||
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- **type**: specifies the type of matrix `A` ( should be one of these: `general`, `upper`, `lower`, `upper-hessenberg`, `symmetric-banded-lower`, `symmetric-banded-upper` or `banded` ). | ||||||
- **KL**: lower band width of `A`. Referenced only if type is `symmetric-banded-lower` or `banded`. | ||||||
- **KU**: upper band width of `A`. Referenced only if type is `symmetric-banded-upper` or `banded`. | ||||||
- **CFROM**: the matrix `A` is multiplied by `CTO / CFROM`. | ||||||
- **CTO**: the matrix `A` is multiplied by `CTO / CFROM`. | ||||||
- **M**: number of rows in matrix `A`. | ||||||
- **N**: number of columns in matrix `A`. | ||||||
- **A**: input [`Float64Array`][mdn-float64array]. | ||||||
- **strideA1**: stride of the first dimension of `A`. | ||||||
- **strideA2**: stride of the second dimension of `A`. | ||||||
- **offsetA**: starting index for `A`. | ||||||
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If `type` is `banded`, `symmetric-banded-lower` or `symmetric-banded-upper` the matrix should be stored in the [`Band storage`][lapack-band-storage] format. | ||||||
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While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, | ||||||
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```javascript | ||||||
var Float64Array = require( '@stdlib/array/float64' ); | ||||||
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var A = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] ); | ||||||
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dlascl.ndarray( 'general', 0, 0, 1.0, 2.0, 3, 2, A, 2, 1, 1 ); | ||||||
// A => <Float64Array>[ 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0 ] | ||||||
``` | ||||||
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</section> | ||||||
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<!-- /.usage --> | ||||||
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<section class="notes"> | ||||||
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## Notes | ||||||
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- `dlascl()` corresponds to the [LAPACK][lapack] routine [`dlascl`][lapack-dlascl]. | ||||||
- If `type` is `banded`, `symmetric-banded-lower` or `symmetric-banded-upper` the matrix should be stored in the [`Band storage`][lapack-band-storage] format. | ||||||
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- If `type` is `banded`, `symmetric-banded-lower` or `symmetric-banded-upper` the matrix should be stored in the [`Band storage`][lapack-band-storage] format. | |
- If `type` is `'banded'`, `'symmetric-banded-lower'`, or `'symmetric-banded-upper'`, the matrix should be stored in the [band storage][lapack-band-storage] format. |
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May be good to provide a brief schematic using math equations in terms of what is meant.
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@aayush0325 Were you thinking you wanted to do the schematics in a separate document or would it make sense to show the matrix equations in this README (e.g., in the intro)?
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/** | ||
* @license Apache-2.0 | ||
* | ||
* Copyright (c) 2025 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. | ||
*/ | ||
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'use strict'; | ||
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// MODULES // | ||
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var bench = require( '@stdlib/bench' ); | ||
var uniform = require( '@stdlib/random/array/uniform' ); | ||
var isnan = require( '@stdlib/math/base/assert/is-nan' ); | ||
var pow = require( '@stdlib/math/base/special/pow' ); | ||
var floor = require( '@stdlib/math/base/special/floor' ); | ||
var pkg = require( './../package.json' ).name; | ||
var dlascl = require( './../lib/dlascl.js' ); | ||
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// VARIABLES // | ||
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var LAYOUTS = [ | ||
'row-major', | ||
'column-major' | ||
]; | ||
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// FUNCTIONS // | ||
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/** | ||
* Creates a benchmark function. | ||
* | ||
* @private | ||
* @param {string} order - storage layout | ||
* @param {PositiveInteger} N - number of rows/columns | ||
* @returns {Function} benchmark function | ||
*/ | ||
function createBenchmark( order, N ) { | ||
var LDA; | ||
var A; | ||
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LDA = N; | ||
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A = uniform( N*N, -10.0, 10.0, { | ||
'dtype': 'float64' | ||
}); | ||
return benchmark; | ||
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/** | ||
* Benchmark function. | ||
* | ||
* @private | ||
* @param {Benchmark} b - benchmark instance | ||
*/ | ||
function benchmark( b ) { | ||
var z; | ||
var i; | ||
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b.tic(); | ||
for ( i = 0; i < b.iterations; i++ ) { | ||
z = dlascl( order, 'general', 0, 0, 1.0, 2.0, N, N, A, LDA ); | ||
if ( isnan( z[ i%z.length ] ) ) { | ||
b.fail( 'should not return NaN' ); | ||
} | ||
} | ||
b.toc(); | ||
if ( isnan( z[ i%z.length ] ) ) { | ||
b.fail( 'should not return NaN' ); | ||
} | ||
b.pass( 'benchmark finished' ); | ||
b.end(); | ||
} | ||
} | ||
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// MAIN // | ||
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/** | ||
* Main execution sequence. | ||
* | ||
* @private | ||
*/ | ||
function main() { | ||
var min; | ||
var max; | ||
var ord; | ||
var N; | ||
var f; | ||
var i; | ||
var k; | ||
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min = 1; // 10^min | ||
max = 6; // 10^max | ||
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for ( k = 0; k < LAYOUTS.length; k++ ) { | ||
ord = LAYOUTS[ k ]; | ||
for ( i = min; i <= max; i++ ) { | ||
N = floor( pow( pow( 10, i ), 1.0/2.0 ) ); | ||
f = createBenchmark( ord, N ); | ||
bench( pkg+'::square_matrix:order='+ord+',size='+(N*N), f ); | ||
} | ||
} | ||
} | ||
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main(); |
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