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10e8866
feat: add lapack/base/dlascl
aayush0325 Jun 29, 2025
5e0cb56
feat: add main exports
aayush0325 Jun 29, 2025
543cbc7
test: add initial tests
aayush0325 Jun 30, 2025
6420a10
test: update imports
aayush0325 Jun 30, 2025
9e43ff5
test: add some tests
aayush0325 Jun 30, 2025
9cf127e
test: update lda validation
aayush0325 Jul 6, 2025
938d704
docs: add ts files
aayush0325 Jul 6, 2025
ec09f0f
docs: add examples
aayush0325 Jul 6, 2025
1d595e0
docs: add readme
aayush0325 Jul 6, 2025
f72cc1b
docs: add readme
aayush0325 Jul 6, 2025
213300c
docs: add package.json
aayush0325 Jul 6, 2025
bd61b1e
docs: add repl.txt
aayush0325 Jul 6, 2025
cd982b0
bench: add benchmarks
aayush0325 Jul 6, 2025
e833c89
test: add tests
aayush0325 Jul 6, 2025
1b9c79e
test: add tests
aayush0325 Jul 6, 2025
31b4286
test: add all tests
aayush0325 Jul 6, 2025
1940240
test: add ndarray tests
aayush0325 Jul 6, 2025
f60ca96
chore: cleanup
aayush0325 Jul 6, 2025
2f26be3
refactor: split base into functions
aayush0325 Jul 6, 2025
b30f33f
refactor: add cont statements
aayush0325 Jul 6, 2025
382580e
Merge remote-tracking branch 'upstream/develop' into lapack-dlascl
stdlib-bot Jul 9, 2025
bd9df6b
refactor: optimise loops
aayush0325 Jul 9, 2025
fefbdef
chore: code review
aayush0325 Aug 19, 2025
54696ce
chore: code review
aayush0325 Aug 19, 2025
e4d4501
docs: add intro section
aayush0325 Aug 19, 2025
0500a86
refactor: optmized loops
aayush0325 Nov 19, 2025
ec95268
refactor: use lesser variables
aayush0325 Nov 19, 2025
29f9c54
fix: critical bug! use correct indexing formula
aayush0325 Nov 19, 2025
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249 changes: 249 additions & 0 deletions lib/node_modules/@stdlib/lapack/base/dlascl/README.md
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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.

-->

# dlascl

> LAPACK routine to multiply a real M by N matrix `A` by a real scalar `CTO/CFROM`.

<section class="usage">

## Usage

```javascript
var dlascl = require( '@stdlib/lapack/base/dlascl' );
```

#### dlascl( order, type, KL, KU, CFROM, CTO, M, N, A, LDA )

Multiplies a real M by N matrix `A` by a real scalar `CTO/CFROM`.

```javascript
var Float64Array = require( '@stdlib/array/float64' );

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 ] ]

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 ]
```

The function has the following parameters:

- **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` ).
- **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].
- **LDA**: stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`).

If `type` is `banded`, `symmetric-banded-lower` or `symmetric-banded-upper` the matrix should be stored in the [`Band storage`][lapack-band-storage] format.

Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.

<!-- eslint-disable stdlib/capitalized-comments -->

```javascript
var Float64Array = require( '@stdlib/array/float64' );

// Initial arrays...
var A0 = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );

// Create offset views...
var A1 = new Float64Array( A0.buffer, A0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

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 ]
```

#### dlascl.ndarray( type, KL, KU, CFROM, CTO, M, N, A, strideA1, strideA2, offsetA )

Multiplies a real M by N matrix `A` by a real scalar `CTO/CFROM` using alternative indexing semantics.

```javascript
var Float64Array = require( '@stdlib/array/float64' );

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 ] ]

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 ]
```

The function has the following additional parameters:

- **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`.

If `type` is `banded`, `symmetric-banded-lower` or `symmetric-banded-upper` the matrix should be stored in the [`Band storage`][lapack-band-storage] format.

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,

```javascript
var Float64Array = require( '@stdlib/array/float64' );

var A = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );

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 ]
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

- `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.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var Float64Array = require( '@stdlib/array/float64' );
var dlascl = require( '@stdlib/lapack/base/dlascl' );

// Define a matrix A (3x2 in row-major order):
var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
console.log( 'Initial A (row-major): ', A );

dlascl( 'row-major', 'general', 0, 0, 1.0, 2.0, 3, 2, A, 2 );
console.log( 'Scaled A (row-major): ', A );
```

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

### Usage

```c
TODO
```

#### TODO

TODO.

```c
TODO
```

TODO

```c
TODO
```

</section>

<!-- /.usage -->

<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- C API usage examples. -->

<section class="examples">

### Examples

```c
TODO
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

<!-- 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">

[lapack]: https://www.netlib.org/lapack/explore-html/

[lapack-dlascl]: https://www.netlib.org/lapack/explore-html-3.6.1/d7/d43/group__aux_o_t_h_e_rauxiliary_ga7bce4c35ec5a86ee0bfdd15c476d99c8.html

[lapack-band-storage]: https://www.netlib.org/lapack/lug/node124.html

[mdn-float64array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float64Array

[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

</section>

<!-- /.links -->
117 changes: 117 additions & 0 deletions lib/node_modules/@stdlib/lapack/base/dlascl/benchmark/benchmark.js
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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.
*/

'use strict';

// MODULES //

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' );


// VARIABLES //

var LAYOUTS = [
'row-major',
'column-major'
];


// FUNCTIONS //

/**
* 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;

LDA = N;

A = uniform( N*N, -10.0, 10.0, {
'dtype': 'float64'
});
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 = 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();
}
}


// MAIN //

/**
* Main execution sequence.
*
* @private
*/
function main() {
var min;
var max;
var ord;
var N;
var f;
var i;
var k;

min = 1; // 10^min
max = 6; // 10^max

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 );
}
}
}

main();
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