@stdlib/math-base-special-fast-alpha-max-plus-beta

Alpha Max Plus Beta Min

$NPM version$ $Build Status$ $Coverage Status$

Compute the hypotenuse using the alpha max plus beta min algorithm.

Installation

npm install @stdlib/math-base-special-fast-alpha-max-plus-beta-min

Usage

var ampbm = require( '@stdlib/math-base-special-fast-alpha-max-plus-beta-min' );

ampbm( x, y )

Computes the hypotenuse using the alpha max plus beta min algorithm.

var h = ampbm( -5.0, 12.0 );
// returns ~13.5

ampbm.factory( alpha, beta, [nonnegative[, ints]] )

Returns a function for computing the hypotenuse using coefficients alpha and beta.

var hypot = ampbm.factory( 1.0, 0.5 );

var h = hypot( -5.0, 12.0 );
// returns 14.5

If the returned function should only expect nonnegative arguments, set the nonnegative argument to true.

var hypot = ampbm.factory( 1.0, 0.5, true );

var h = hypot( 5.0, 12.0 );
// returns 14.5

If the returned function should only expect signed 32-bit integers, set the ints argument to true.

var hypot = ampbm.factory( 1.0, 0.5, false, true );

var h = hypot( -5.0, 12.0 );
// returns 14

If the returned function should only expect unsigned 32-bit integer valued arguments, set the nonnegative and ints arguments to true.

var hypot = ampbm.factory( 1.0, 0.5, true, true );

var h = hypot( 5.0, 12.0 );
// returns 14

Notes

The algorithm computes only an approximation. For precise results, use hypot.

Examples

var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var ampbm = require( '@stdlib/math-base-special-fast-alpha-max-plus-beta-min' );

var x;
var y;
var h;
var i;

for ( i = 0; i < 100; i++ ) {
    x = round( randu()*100.0 ) - 50.0;
    y = round( randu()*100.0 ) - 50.0;
    h = ampbm( x, y );
    console.log( 'hypot(%d,%d) = %d', x, y, h );
}

References

Lyons, Richard G. 2011. Understanding Digital Signal Processing, 3rd Edition. Prentice Hall.

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.