uclibc-ng/libm/double/eigens.c

/*							eigens.c
 *
 *	Eigenvalues and eigenvectors of a real symmetric matrix
 *
 *
 *
 * SYNOPSIS:
 *
 * int n;
 * double A[n*(n+1)/2], EV[n*n], E[n];
 * void eigens( A, EV, E, n );
 *
 *
 *
 * DESCRIPTION:
 *
 * The algorithm is due to J. vonNeumann.
 *
 * A[] is a symmetric matrix stored in lower triangular form.
 * That is, A[ row, column ] = A[ (row*row+row)/2 + column ]
 * or equivalently with row and column interchanged.  The
 * indices row and column run from 0 through n-1.
 *
 * EV[] is the output matrix of eigenvectors stored columnwise.
 * That is, the elements of each eigenvector appear in sequential
 * memory order.  The jth element of the ith eigenvector is
 * EV[ n*i+j ] = EV[i][j].
 *
 * E[] is the output matrix of eigenvalues.  The ith element
 * of E corresponds to the ith eigenvector (the ith row of EV).
 *
 * On output, the matrix A will have been diagonalized and its
 * orginal contents are destroyed.
 *
 * ACCURACY:
 *
 * The error is controlled by an internal parameter called RANGE
 * which is set to 1e-10.  After diagonalization, the
 * off-diagonal elements of A will have been reduced by
 * this factor.
 *
 * ERROR MESSAGES:
 *
 * None.
 *
 */

#include <math.h>
#ifdef ANSIPROT
extern double sqrt ( double );
extern double fabs ( double );
#else
double sqrt(), fabs();
#endif

void eigens( A, RR, E, N )
double A[], RR[], E[];
int N;
{
int IND, L, LL, LM, M, MM, MQ, I, J, IA, LQ;
int IQ, IM, IL, NLI, NMI;
double ANORM, ANORMX, AIA, THR, ALM, ALL, AMM, X, Y;
double SINX, SINX2, COSX, COSX2, SINCS, AIL, AIM;
double RLI, RMI;
static double RANGE = 1.0e-10; /*3.0517578e-5;*/


/* Initialize identity matrix in RR[] */
for( J=0; J<N*N; J++ )
	RR[J] = 0.0;
MM = 0;
for( J=0; J<N; J++ )
	{
	RR[MM + J] = 1.0;
	MM += N;
	}

ANORM=0.0;
for( I=0; I<N; I++ )
	{
	for( J=0; J<N; J++ )
		{
		if( I != J )
			{
			IA = I + (J*J+J)/2;
			AIA = A[IA];
			ANORM += AIA * AIA;
			}
		}
	}
if( ANORM <= 0.0 )
	goto done;
ANORM = sqrt( ANORM + ANORM );
ANORMX = ANORM * RANGE / N;
THR = ANORM;

while( THR > ANORMX )
{
THR=THR/N;

do
{ /* while IND != 0 */
IND = 0;

for( L=0; L<N-1; L++ )
	{

for( M=L+1; M<N; M++ )
	{
	MQ=(M*M+M)/2;
	LM=L+MQ;
	ALM=A[LM];
	if( fabs(ALM) < THR )
		continue;

	IND=1;
	LQ=(L*L+L)/2;
	LL=L+LQ;
	MM=M+MQ;
	ALL=A[LL];
	AMM=A[MM];
	X=(ALL-AMM)/2.0;
	Y=-ALM/sqrt(ALM*ALM+X*X);
	if(X < 0.0)
		Y=-Y;
	SINX = Y / sqrt( 2.0 * (1.0 + sqrt( 1.0-Y*Y)) );
	SINX2=SINX*SINX;
	COSX=sqrt(1.0-SINX2);
	COSX2=COSX*COSX;
	SINCS=SINX*COSX;

/*	   ROTATE L AND M COLUMNS */
for( I=0; I<N; I++ )
	{
	IQ=(I*I+I)/2;
	if( (I != M) && (I != L) )
		{
		if(I > M)
			IM=M+IQ;
		else
			IM=I+MQ;
		if(I >= L)
			IL=L+IQ;
		else
			IL=I+LQ;
		AIL=A[IL];
		AIM=A[IM];
		X=AIL*COSX-AIM*SINX;
		A[IM]=AIL*SINX+AIM*COSX;
		A[IL]=X;
		}
	NLI = N*L + I;
	NMI = N*M + I;
	RLI = RR[ NLI ];
	RMI = RR[ NMI ];
	RR[NLI]=RLI*COSX-RMI*SINX;
	RR[NMI]=RLI*SINX+RMI*COSX;
	}

	X=2.0*ALM*SINCS;
	A[LL]=ALL*COSX2+AMM*SINX2-X;
	A[MM]=ALL*SINX2+AMM*COSX2+X;
	A[LM]=(ALL-AMM)*SINCS+ALM*(COSX2-SINX2);
	} /* for M=L+1 to N-1 */
	} /* for L=0 to N-2 */

	}
while( IND != 0 );

} /* while THR > ANORMX */

done:	;

/* Extract eigenvalues from the reduced matrix */
L=0;
for( J=1; J<=N; J++ )
	{
	L=L+J;
	E[J-1]=A[L-1];
	}
}