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dlacpy

Copy all or part of a matrix A to another matrix B.

Usage

var dlacpy = require( '@stdlib/lapack-base-dlacpy' );

dlacpy( order, uplo, M, N, A, LDA, B, LDB )

Copies all or part of a matrix A to another matrix B.

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

var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
var B = new Float64Array( 4 );

dlacpy( 'row-major', 'all', 2, 2, A, 2, B, 2 );
// B => <Float64Array>[ 1.0, 2.0, 3.0, 4.0 ]

The function has the following parameters:

• order: storage layout.
• uplo: specifies whether to copy the upper or lower triangular/trapezoidal part of a matrix A.
• M: number of rows in A.
• N: number of columns in A.
• A: input Float64Array.
• LDA: stride of the first dimension of A (a.k.a., leading dimension of the matrix A).
• B: output Float64Array.
• LDB: stride of the first dimension of B (a.k.a., leading dimension of the matrix B).

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

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

// Initial arrays...
var A0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
var B0 = new Float64Array( 5 );

// Create offset views...
var A1 = new Float64Array( A0.buffer, A0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var B1 = new Float64Array( B0.buffer, B0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

dlacpy( 'row-major', 'all', 2, 2, A1, 2, B1, 2 );
// B0 => <Float64Array>[ 0.0, 2.0, 3.0, 4.0, 5.0 ]

dlacpy.ndarray( uplo, M, N, A, sa1, sa2, oa, B, sb1, sb2, ob )

Copies all or part of a matrix A to another matrix B using alternative indexing semantics.

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

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

dlacpy.ndarray( 'all', 2, 2, A, 2, 1, 0, B, 2, 1, 0 );
// B => <Float64Array>[ 1.0, 2.0, 3.0, 4.0 ]

The function has the following parameters:

• uplo: specifies whether to copy the upper or lower triangular/trapezoidal part of a matrix A.
• M: number of rows in A.
• N: number of columns in A.
• A: input Float64Array.
• sa1: stride of the first dimension of A.
• sa2: stride of the second dimension of A.
• oa: starting index for A.
• B: output Float64Array.
• sb1: stride of the first dimension of B.
• sb2: stride of the second dimension of B.
• ob: starting index for B.

While typed array views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example,

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

var A = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0 ] );
var B = new Float64Array( [ 0.0, 0.0, 11.0, 312.0, 53.0, 412.0 ] );

dlacpy.ndarray( 'all', 2, 2, A, 2, 1, 1, B, 2, 1, 2 );
// B => <Float64Array>[ 0.0, 0.0, 1.0, 2.0, 3.0, 4.0 ]

Notes

• dlacpy() corresponds to the LAPACK routine dlacpy.

Examples

var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var uniform = require( '@stdlib/random-array-discrete-uniform' );
var numel = require( '@stdlib/ndarray-base-numel' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var dlacpy = require( '@stdlib/lapack-base-dlacpy' );

var shape = [ 5, 8 ];
var order = 'row-major';
var strides = shape2strides( shape, order );

var N = numel( shape );

var A = uniform( N, -10, 10, {
    'dtype': 'float64'
});
console.log( ndarray2array( A, shape, strides, 0, order ) );

var B = uniform( N, -10, 10, {
    'dtype': 'float64'
});
console.log( ndarray2array( B, shape, strides, 0, order ) );

dlacpy( order, 'all', shape[ 0 ], shape[ 1 ], A, strides[ 0 ], B, strides[ 0 ] );
console.log( ndarray2array( B, shape, strides, 0, order ) );

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C APIs

Installation

npm install @stdlib/lapack-base-dlacpy

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

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Examples

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Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.