#include "blaswrap.h"
#include "f2c.h"

/* Subroutine */ int slarrr_(integer *n, real *d__, real *e, integer *info)
{
/*  -- LAPACK auxiliary routine (version 3.1) --   
       Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd..   
       November 2006   



    Purpose   
    =======   

    Perform tests to decide whether the symmetric tridiagonal matrix T   
    warrants expensive computations which guarantee high relative accuracy   
    in the eigenvalues.   

    Arguments   
    =========   

    N       (input) INTEGER   
            The order of the matrix. N > 0.   

    D       (input) REAL             array, dimension (N)   
            The N diagonal elements of the tridiagonal matrix T.   

    E       (input/output) REAL             array, dimension (N)   
            On entry, the first (N-1) entries contain the subdiagonal   
            elements of the tridiagonal matrix T; E(N) is set to ZERO.   

    INFO    (output) INTEGER   
            INFO = 0(default) : the matrix warrants computations preserving   
                                relative accuracy.   
            INFO = 1          : the matrix warrants computations guaranteeing   
                                only absolute accuracy.   

    Further Details   
    ===============   

    Based on contributions by   
       Beresford Parlett, University of California, Berkeley, USA   
       Jim Demmel, University of California, Berkeley, USA   
       Inderjit Dhillon, University of Texas, Austin, USA   
       Osni Marques, LBNL/NERSC, USA   
       Christof Voemel, University of California, Berkeley, USA   

    =====================================================================   


       As a default, do NOT go for relative-accuracy preserving computations.   
       Parameter adjustments */
    /* System generated locals */
    integer i__1;
    real r__1;
    /* Builtin functions */
    double sqrt(doublereal);
    /* Local variables */
    static integer i__;
    static real eps, tmp, tmp2, rmin, offdig;
    extern doublereal slamch_(char *);
    static real safmin;
    static logical yesrel;
    static real smlnum, offdig2;

    --e;
    --d__;

    /* Function Body */
    *info = 1;
    safmin = slamch_("Safe minimum");
    eps = slamch_("Precision");
    smlnum = safmin / eps;
    rmin = sqrt(smlnum);
/*     Tests for relative accuracy   

       Test for scaled diagonal dominance   
       Scale the diagonal entries to one and check whether the sum of the   
       off-diagonals is less than one   

       The sdd relative error bounds have a 1/(1- 2*x) factor in them,   
       x = max(OFFDIG + OFFDIG2), so when x is close to 1/2, no relative   
       accuracy is promised.  In the notation of the code fragment below,   
       1/(1 - (OFFDIG + OFFDIG2)) is the condition number.   
       We don't think it is worth going into "sdd mode" unless the relative   
       condition number is reasonable, not 1/macheps.   
       The threshold should be compatible with other thresholds used in the   
       code. We set  OFFDIG + OFFDIG2 <= .999 =: RELCOND, it corresponds   
       to losing at most 3 decimal digits: 1 / (1 - (OFFDIG + OFFDIG2)) <= 1000   
       instead of the current OFFDIG + OFFDIG2 < 1 */

    yesrel = TRUE_;
    offdig = 0.f;
    tmp = sqrt((dabs(d__[1])));
    if (tmp < rmin) {
	yesrel = FALSE_;
    }
    if (! yesrel) {
	goto L11;
    }
    i__1 = *n;
    for (i__ = 2; i__ <= i__1; ++i__) {
	tmp2 = sqrt((r__1 = d__[i__], dabs(r__1)));
	if (tmp2 < rmin) {
	    yesrel = FALSE_;
	}
	if (! yesrel) {
	    goto L11;
	}
	offdig2 = (r__1 = e[i__ - 1], dabs(r__1)) / (tmp * tmp2);
	if (offdig + offdig2 >= .999f) {
	    yesrel = FALSE_;
	}
	if (! yesrel) {
	    goto L11;
	}
	tmp = tmp2;
	offdig = offdig2;
/* L10: */
    }
L11:
    if (yesrel) {
	*info = 0;
	return 0;
    } else {
    }


/*     *** MORE TO BE IMPLEMENTED ***   


       Test if the lower bidiagonal matrix L from T = L D L^T   
       (zero shift facto) is well conditioned   


       Test if the upper bidiagonal matrix U from T = U D U^T   
       (zero shift facto) is well conditioned.   
       In this case, the matrix needs to be flipped and, at the end   
       of the eigenvector computation, the flip needs to be applied   
       to the computed eigenvectors (and the support) */


    return 0;

/*     END OF SLARRR */

} /* slarrr_ */