Skip to content

Latest commit

 

History

History
357 lines (219 loc) · 10.8 KB

README.md

File metadata and controls

357 lines (219 loc) · 10.8 KB
Raw
About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

dpttrf

NPM version Build Status Coverage Status

Compute the L * D * L^T factorization of a real symmetric positive definite tridiagonal matrix A.

Installation

npm install @stdlib/lapack-base-dpttrf

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

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

dpttrf( N, D, E )

Computes the L * D * L^T factorization of a real symmetric positive definite tridiagonal matrix A.

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

var D = new Float64Array( [ 4.0, 5.0, 6.0 ] );
var E = new Float64Array( [ 1.0, 2.0 ] );

dpttrf( 3, D, E );
// D => <Float64Array>[ 4, 4.75, ~5.15789 ]
// E => <Float64Array>[ 0.25, ~0.4210 ]

The function has the following parameters:

  • N: order of matrix A.
  • D: the N diagonal elements of A as a Float64Array.
  • E: the N-1 subdiagonal elements of A as a Float64Array.

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 D0 = new Float64Array( [ 0.0, 4.0, 5.0, 6.0 ] );
var E0 = new Float64Array( [ 0.0, 1.0, 2.0 ] );

// Create offset views...
var D1 = new Float64Array( D0.buffer, D0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var E1 = new Float64Array( E0.buffer, E0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

dpttrf( 3, D1, E1 );
// D0 => <Float64Array>[ 0.0, 4.0, 4.75, ~5.15789 ]
// E0 => <Float64Array>[ 0.0, 0.25, ~0.4210 ]

dpttrf.ndarray( N, D, strideD, offsetD, E, strideE, offsetE )

Computes the L * D * L^T factorization of a real symmetric positive definite tridiagonal matrix A using alternative indexing semantics.

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

var D = new Float64Array( [ 4.0, 5.0, 6.0 ] );
var E = new Float64Array( [ 1.0, 2.0 ] );

dpttrf.ndarray( 3, D, 1, 0, E, 1, 0 );
// D => <Float64Array>[ 4, 4.75, ~5.15789 ]
// E => <Float64Array>[ 0.25, ~0.4210 ]

The function has the following additional parameters:

  • strideD: stride length for D.
  • offsetD: starting index for D.
  • strideE: stride length for E.
  • offsetE: starting index for E.

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

dpttrf.ndarray( 3, D, 1, 1, E, 1, 1 );
// D => <Float64Array>[ 0.0, 4.0, 4.75, ~5.15789 ]
// E => <Float64Array>[ 0.0, 0.25, ~0.4210 ]

Notes

  • Both functions mutate the input arrays D and E.

  • Both functions return a status code indicating success or failure. A status code indicates the following conditions:

    • 0: factorization was successful.
    • <0: the k-th argument had an illegal value, where -k equals the status code value.
    • 0 < k < N: the leading principal minor of order k is not positive and factorization could not be completed, where k equals the status code value.
    • N: the leading principal minor of order N is not positive, and factorization was completed.

  • dpttrf() corresponds to the LAPACK routine dpttrf.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var dpttrf = require( '@stdlib/lapack-base-dpttrf' );

var opts = {
    'dtype': 'float64'
};
var D = discreteUniform( 5, 1, 5, opts );
console.log( D );

var E = discreteUniform( D.length-1, 1, 5, opts );
console.log( E );

// Perform the `L * D * L^T` factorization:
var info = dpttrf( D.length, D, E );
console.log( D );
console.log( E );
console.log( info );

C APIs

Usage

TODO

TODO

TODO.

TODO

TODO

TODO

Examples

TODO

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

Chat

License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.