/*
 * This code was (mostly) written by Ken Turkowski, who said:
 *
 * Oh, that. I wrote it in college the first time. It's open source - I think I
 * posted it after seeing so many people solve equations by inverting matrices
 * by computing minors na�ly.
 * -Ken
 *
 * The views represented here are mine and are not necessarily shared by
 * my employer.
   	Ken Turkowski			turk@apple.com
	Immersive Media Technologist 	http://www.worldserver.com/turk/
	Apple Computer, Inc.
	1 Infinite Loop, MS 302-3VR
	Cupertino, CA 95014
 */



/* This module solves linear equations in several variables (Ax = b) using
 * LU decomposition with partial pivoting and row equilibration.  Although
 * slightly more work than Gaussian elimination, it is faster for solving
 * several equations using the same coefficient matrix.  It is
 * particularly useful for matrix inversion, by sequentially solving the
 * equations with the columns of the unit matrix.
 *
 * lu_decompose() decomposes the coefficient matrix into the LU matrix,
 * and lu_solve() solves the series of matrix equations using the
 * previous LU decomposition.
 *
 *	Ken Turkowski (apple!turk)
 *	written 3/2/79, revised and enhanced 8/9/83.
 */

#include <stdlib.h>
#include <neato.h>

static double *scales;
static double **lu;
static int *ps;

/* lu_decompose() decomposes the coefficient matrix A into upper and lower
 * triangular matrices, the composite being the LU matrix.
 *
 * The arguments are:
 *
 *	a - the (n x n) coefficient matrix
 *	n - the order of the matrix
 *
 *  1 is returned if the decomposition was successful,
 *  and 0 is returned if the coefficient matrix is singular.
 */

int lu_decompose(double **a, int n)
{
	register int i, j, k;
	int pivotindex = 0;
	double pivot, biggest, mult, tempf;
	double fabs();

	if (lu) free_array(lu); lu = new_array(n,n,0.0);
	if (ps) free(ps); ps = N_NEW(n,int);
	if (scales) free(scales); scales = N_NEW(n,double);

	for (i = 0; i < n; i++) {	/* For each row */
	    /* Find the largest element in each row for row equilibration */
	    biggest = 0.0;
	    for (j = 0; j < n; j++)
		if (biggest < (tempf = fabs(lu[i][j] = a[i][j])))
		    biggest  = tempf;
	    if (biggest != 0.0)
		scales[i] = 1.0 / biggest;
	    else {
		scales[i] = 0.0;
		return(0);	/* Zero row: singular matrix */
	    }
	    ps[i] = i;		/* Initialize pivot sequence */
	}

	for (k = 0; k < n-1; k++) {	/* For each column */
	    /* Find the largest element in each column to pivot around */
	    biggest = 0.0;
	    for (i = k; i < n; i++) {
		if (biggest < (tempf = fabs(lu[ps[i]][k]) * scales[ps[i]])) {
		    biggest = tempf;
		    pivotindex = i;
		}
	    }
	    if (biggest == 0.0)
		return(0);	/* Zero column: singular matrix */
	    if (pivotindex != k) {	/* Update pivot sequence */
		j = ps[k];
		ps[k] = ps[pivotindex];
		ps[pivotindex] = j;
	    }

	    /* Pivot, eliminating an extra variable  each time */
	    pivot = lu[ps[k]][k];
	    for (i = k+1; i < n; i++) {
		lu[ps[i]][k] = mult = lu[ps[i]][k] / pivot;
		if (mult != 0.0) {
		    for (j = k+1; j < n; j++)
			lu[ps[i]][j] -= mult * lu[ps[k]][j];
		}
	    }
	}

	if (lu[ps[n-1]][n-1] == 0.0)
	    return(0);		/* Singular matrix */
	return(1);
}

/* lu_solve() solves the linear equation (Ax = b) after the matrix A has
 * been decomposed with lu_decompose() into the lower and upper triangular
 * matrices L and U.
 *
 * The arguments are:
 *
 *	x - the solution vector
 *	b - the constant vector
 *	n - the order of the equation
*/

void lu_solve(double *x, double *b, int n)
{
	register int i, j;
	double dot;

	/* Vector reduction using U triangular matrix */
	for (i = 0; i < n; i++) {
	    dot = 0.0;
	    for (j = 0; j < i; j++)
		dot += lu[ps[i]][j] * x[j];
	    x[i] = b[ps[i]] - dot;
	}

	/* Back substitution, in L triangular matrix */
	for (i = n-1; i >= 0; i--) {
	    dot = 0.0;
	    for (j = i+1; j < n; j++)
		dot += lu[ps[i]][j] * x[j];
	    x[i] = (x[i] - dot) / lu[ps[i]][i];
	}
}