#include "blaswrap.h" #include "f2c.h" /* Subroutine */ int dlatps_(char *uplo, char *trans, char *diag, char * normin, integer *n, doublereal *ap, doublereal *x, doublereal *scale, doublereal *cnorm, integer *info) { /* -- LAPACK auxiliary routine (version 3.1) -- Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. November 2006 Purpose ======= DLATPS solves one of the triangular systems A *x = s*b or A'*x = s*b with scaling to prevent overflow, where A is an upper or lower triangular matrix stored in packed form. Here A' denotes the transpose of A, x and b are n-element vectors, and s is a scaling factor, usually less than or equal to 1, chosen so that the components of x will be less than the overflow threshold. If the unscaled problem will not cause overflow, the Level 2 BLAS routine DTPSV is called. If the matrix A is singular (A(j,j) = 0 for some j), then s is set to 0 and a non-trivial solution to A*x = 0 is returned. Arguments ========= UPLO (input) CHARACTER*1 Specifies whether the matrix A is upper or lower triangular. = 'U': Upper triangular = 'L': Lower triangular TRANS (input) CHARACTER*1 Specifies the operation applied to A. = 'N': Solve A * x = s*b (No transpose) = 'T': Solve A'* x = s*b (Transpose) = 'C': Solve A'* x = s*b (Conjugate transpose = Transpose) DIAG (input) CHARACTER*1 Specifies whether or not the matrix A is unit triangular. = 'N': Non-unit triangular = 'U': Unit triangular NORMIN (input) CHARACTER*1 Specifies whether CNORM has been set or not. = 'Y': CNORM contains the column norms on entry = 'N': CNORM is not set on entry. On exit, the norms will be computed and stored in CNORM. N (input) INTEGER The order of the matrix A. N >= 0. AP (input) DOUBLE PRECISION array, dimension (N*(N+1)/2) The upper or lower triangular matrix A, packed columnwise in a linear array. The j-th column of A is stored in the array AP as follows: if UPLO = 'U', AP(i + (j-1)*j/2) = A(i,j) for 1<=i<=j; if UPLO = 'L', AP(i + (j-1)*(2n-j)/2) = A(i,j) for j<=i<=n. X (input/output) DOUBLE PRECISION array, dimension (N) On entry, the right hand side b of the triangular system. On exit, X is overwritten by the solution vector x. SCALE (output) DOUBLE PRECISION The scaling factor s for the triangular system A * x = s*b or A'* x = s*b. If SCALE = 0, the matrix A is singular or badly scaled, and the vector x is an exact or approximate solution to A*x = 0. CNORM (input or output) DOUBLE PRECISION array, dimension (N) If NORMIN = 'Y', CNORM is an input argument and CNORM(j) contains the norm of the off-diagonal part of the j-th column of A. If TRANS = 'N', CNORM(j) must be greater than or equal to the infinity-norm, and if TRANS = 'T' or 'C', CNORM(j) must be greater than or equal to the 1-norm. If NORMIN = 'N', CNORM is an output argument and CNORM(j) returns the 1-norm of the offdiagonal part of the j-th column of A. INFO (output) INTEGER = 0: successful exit < 0: if INFO = -k, the k-th argument had an illegal value Further Details ======= ======= A rough bound on x is computed; if that is less than overflow, DTPSV is called, otherwise, specific code is used which checks for possible overflow or divide-by-zero at every operation. A columnwise scheme is used for solving A*x = b. The basic algorithm if A is lower triangular is x[1:n] := b[1:n] for j = 1, ..., n x(j) := x(j) / A(j,j) x[j+1:n] := x[j+1:n] - x(j) * A[j+1:n,j] end Define bounds on the components of x after j iterations of the loop: M(j) = bound on x[1:j] G(j) = bound on x[j+1:n] Initially, let M(0) = 0 and G(0) = max{x(i), i=1,...,n}. Then for iteration j+1 we have M(j+1) <= G(j) / | A(j+1,j+1) | G(j+1) <= G(j) + M(j+1) * | A[j+2:n,j+1] | <= G(j) ( 1 + CNORM(j+1) / | A(j+1,j+1) | ) where CNORM(j+1) is greater than or equal to the infinity-norm of column j+1 of A, not counting the diagonal. Hence G(j) <= G(0) product ( 1 + CNORM(i) / | A(i,i) | ) 1<=i<=j and |x(j)| <= ( G(0) / |A(j,j)| ) product ( 1 + CNORM(i) / |A(i,i)| ) 1<=i< j Since |x(j)| <= M(j), we use the Level 2 BLAS routine DTPSV if the reciprocal of the largest M(j), j=1,..,n, is larger than max(underflow, 1/overflow). The bound on x(j) is also used to determine when a step in the columnwise method can be performed without fear of overflow. If the computed bound is greater than a large constant, x is scaled to prevent overflow, but if the bound overflows, x is set to 0, x(j) to 1, and scale to 0, and a non-trivial solution to A*x = 0 is found. Similarly, a row-wise scheme is used to solve A'*x = b. The basic algorithm for A upper triangular is for j = 1, ..., n x(j) := ( b(j) - A[1:j-1,j]' * x[1:j-1] ) / A(j,j) end We simultaneously compute two bounds G(j) = bound on ( b(i) - A[1:i-1,i]' * x[1:i-1] ), 1<=i<=j M(j) = bound on x(i), 1<=i<=j The initial values are G(0) = 0, M(0) = max{b(i), i=1,..,n}, and we add the constraint G(j) >= G(j-1) and M(j) >= M(j-1) for j >= 1. Then the bound on x(j) is M(j) <= M(j-1) * ( 1 + CNORM(j) ) / | A(j,j) | <= M(0) * product ( ( 1 + CNORM(i) ) / |A(i,i)| ) 1<=i<=j and we can safely call DTPSV if 1/M(n) and 1/G(n) are both greater than max(underflow, 1/overflow). ===================================================================== Parameter adjustments */ /* Table of constant values */ static integer c__1 = 1; static doublereal c_b36 = .5; /* System generated locals */ integer i__1, i__2, i__3; doublereal d__1, d__2, d__3; /* Local variables */ static integer i__, j, ip; static doublereal xj, rec, tjj; static integer jinc, jlen; extern doublereal ddot_(integer *, doublereal *, integer *, doublereal *, integer *); static doublereal xbnd; static integer imax; static doublereal tmax, tjjs, xmax, grow, sumj; extern /* Subroutine */ int dscal_(integer *, doublereal *, doublereal *, integer *); extern logical lsame_(char *, char *); static doublereal tscal, uscal; extern doublereal dasum_(integer *, doublereal *, integer *); static integer jlast; extern /* Subroutine */ int daxpy_(integer *, doublereal *, doublereal *, integer *, doublereal *, integer *); static logical upper; extern /* Subroutine */ int dtpsv_(char *, char *, char *, integer *, doublereal *, doublereal *, integer *); extern doublereal dlamch_(char *); extern integer idamax_(integer *, doublereal *, integer *); extern /* Subroutine */ int xerbla_(char *, integer *); static doublereal bignum; static logical notran; static integer jfirst; static doublereal smlnum; static logical nounit; --cnorm; --x; --ap; /* Function Body */ *info = 0; upper = lsame_(uplo, "U"); notran = lsame_(trans, "N"); nounit = lsame_(diag, "N"); /* Test the input parameters. */ if (! upper && ! lsame_(uplo, "L")) { *info = -1; } else if (! notran && ! lsame_(trans, "T") && ! lsame_(trans, "C")) { *info = -2; } else if (! nounit && ! lsame_(diag, "U")) { *info = -3; } else if (! lsame_(normin, "Y") && ! lsame_(normin, "N")) { *info = -4; } else if (*n < 0) { *info = -5; } if (*info != 0) { i__1 = -(*info); xerbla_("DLATPS", &i__1); return 0; } /* Quick return if possible */ if (*n == 0) { return 0; } /* Determine machine dependent parameters to control overflow. */ smlnum = dlamch_("Safe minimum") / dlamch_("Precision"); bignum = 1. / smlnum; *scale = 1.; if (lsame_(normin, "N")) { /* Compute the 1-norm of each column, not including the diagonal. */ if (upper) { /* A is upper triangular. */ ip = 1; i__1 = *n; for (j = 1; j <= i__1; ++j) { i__2 = j - 1; cnorm[j] = dasum_(&i__2, &ap[ip], &c__1); ip += j; /* L10: */ } } else { /* A is lower triangular. */ ip = 1; i__1 = *n - 1; for (j = 1; j <= i__1; ++j) { i__2 = *n - j; cnorm[j] = dasum_(&i__2, &ap[ip + 1], &c__1); ip = ip + *n - j + 1; /* L20: */ } cnorm[*n] = 0.; } } /* Scale the column norms by TSCAL if the maximum element in CNORM is greater than BIGNUM. */ imax = idamax_(n, &cnorm[1], &c__1); tmax = cnorm[imax]; if (tmax <= bignum) { tscal = 1.; } else { tscal = 1. / (smlnum * tmax); dscal_(n, &tscal, &cnorm[1], &c__1); } /* Compute a bound on the computed solution vector to see if the Level 2 BLAS routine DTPSV can be used. */ j = idamax_(n, &x[1], &c__1); xmax = (d__1 = x[j], abs(d__1)); xbnd = xmax; if (notran) { /* Compute the growth in A * x = b. */ if (upper) { jfirst = *n; jlast = 1; jinc = -1; } else { jfirst = 1; jlast = *n; jinc = 1; } if (tscal != 1.) { grow = 0.; goto L50; } if (nounit) { /* A is non-unit triangular. Compute GROW = 1/G(j) and XBND = 1/M(j). Initially, G(0) = max{x(i), i=1,...,n}. */ grow = 1. / max(xbnd,smlnum); xbnd = grow; ip = jfirst * (jfirst + 1) / 2; jlen = *n; i__1 = jlast; i__2 = jinc; for (j = jfirst; i__2 < 0 ? j >= i__1 : j <= i__1; j += i__2) { /* Exit the loop if the growth factor is too small. */ if (grow <= smlnum) { goto L50; } /* M(j) = G(j-1) / abs(A(j,j)) */ tjj = (d__1 = ap[ip], abs(d__1)); /* Computing MIN */ d__1 = xbnd, d__2 = min(1.,tjj) * grow; xbnd = min(d__1,d__2); if (tjj + cnorm[j] >= smlnum) { /* G(j) = G(j-1)*( 1 + CNORM(j) / abs(A(j,j)) ) */ grow *= tjj / (tjj + cnorm[j]); } else { /* G(j) could overflow, set GROW to 0. */ grow = 0.; } ip += jinc * jlen; --jlen; /* L30: */ } grow = xbnd; } else { /* A is unit triangular. Compute GROW = 1/G(j), where G(0) = max{x(i), i=1,...,n}. Computing MIN */ d__1 = 1., d__2 = 1. / max(xbnd,smlnum); grow = min(d__1,d__2); i__2 = jlast; i__1 = jinc; for (j = jfirst; i__1 < 0 ? j >= i__2 : j <= i__2; j += i__1) { /* Exit the loop if the growth factor is too small. */ if (grow <= smlnum) { goto L50; } /* G(j) = G(j-1)*( 1 + CNORM(j) ) */ grow *= 1. / (cnorm[j] + 1.); /* L40: */ } } L50: ; } else { /* Compute the growth in A' * x = b. */ if (upper) { jfirst = 1; jlast = *n; jinc = 1; } else { jfirst = *n; jlast = 1; jinc = -1; } if (tscal != 1.) { grow = 0.; goto L80; } if (nounit) { /* A is non-unit triangular. Compute GROW = 1/G(j) and XBND = 1/M(j). Initially, M(0) = max{x(i), i=1,...,n}. */ grow = 1. / max(xbnd,smlnum); xbnd = grow; ip = jfirst * (jfirst + 1) / 2; jlen = 1; i__1 = jlast; i__2 = jinc; for (j = jfirst; i__2 < 0 ? j >= i__1 : j <= i__1; j += i__2) { /* Exit the loop if the growth factor is too small. */ if (grow <= smlnum) { goto L80; } /* G(j) = max( G(j-1), M(j-1)*( 1 + CNORM(j) ) ) */ xj = cnorm[j] + 1.; /* Computing MIN */ d__1 = grow, d__2 = xbnd / xj; grow = min(d__1,d__2); /* M(j) = M(j-1)*( 1 + CNORM(j) ) / abs(A(j,j)) */ tjj = (d__1 = ap[ip], abs(d__1)); if (xj > tjj) { xbnd *= tjj / xj; } ++jlen; ip += jinc * jlen; /* L60: */ } grow = min(grow,xbnd); } else { /* A is unit triangular. Compute GROW = 1/G(j), where G(0) = max{x(i), i=1,...,n}. Computing MIN */ d__1 = 1., d__2 = 1. / max(xbnd,smlnum); grow = min(d__1,d__2); i__2 = jlast; i__1 = jinc; for (j = jfirst; i__1 < 0 ? j >= i__2 : j <= i__2; j += i__1) { /* Exit the loop if the growth factor is too small. */ if (grow <= smlnum) { goto L80; } /* G(j) = ( 1 + CNORM(j) )*G(j-1) */ xj = cnorm[j] + 1.; grow /= xj; /* L70: */ } } L80: ; } if (grow * tscal > smlnum) { /* Use the Level 2 BLAS solve if the reciprocal of the bound on elements of X is not too small. */ dtpsv_(uplo, trans, diag, n, &ap[1], &x[1], &c__1); } else { /* Use a Level 1 BLAS solve, scaling intermediate results. */ if (xmax > bignum) { /* Scale X so that its components are less than or equal to BIGNUM in absolute value. */ *scale = bignum / xmax; dscal_(n, scale, &x[1], &c__1); xmax = bignum; } if (notran) { /* Solve A * x = b */ ip = jfirst * (jfirst + 1) / 2; i__1 = jlast; i__2 = jinc; for (j = jfirst; i__2 < 0 ? j >= i__1 : j <= i__1; j += i__2) { /* Compute x(j) = b(j) / A(j,j), scaling x if necessary. */ xj = (d__1 = x[j], abs(d__1)); if (nounit) { tjjs = ap[ip] * tscal; } else { tjjs = tscal; if (tscal == 1.) { goto L100; } } tjj = abs(tjjs); if (tjj > smlnum) { /* abs(A(j,j)) > SMLNUM: */ if (tjj < 1.) { if (xj > tjj * bignum) { /* Scale x by 1/b(j). */ rec = 1. / xj; dscal_(n, &rec, &x[1], &c__1); *scale *= rec; xmax *= rec; } } x[j] /= tjjs; xj = (d__1 = x[j], abs(d__1)); } else if (tjj > 0.) { /* 0 < abs(A(j,j)) <= SMLNUM: */ if (xj > tjj * bignum) { /* Scale x by (1/abs(x(j)))*abs(A(j,j))*BIGNUM to avoid overflow when dividing by A(j,j). */ rec = tjj * bignum / xj; if (cnorm[j] > 1.) { /* Scale by 1/CNORM(j) to avoid overflow when multiplying x(j) times column j. */ rec /= cnorm[j]; } dscal_(n, &rec, &x[1], &c__1); *scale *= rec; xmax *= rec; } x[j] /= tjjs; xj = (d__1 = x[j], abs(d__1)); } else { /* A(j,j) = 0: Set x(1:n) = 0, x(j) = 1, and scale = 0, and compute a solution to A*x = 0. */ i__3 = *n; for (i__ = 1; i__ <= i__3; ++i__) { x[i__] = 0.; /* L90: */ } x[j] = 1.; xj = 1.; *scale = 0.; xmax = 0.; } L100: /* Scale x if necessary to avoid overflow when adding a multiple of column j of A. */ if (xj > 1.) { rec = 1. / xj; if (cnorm[j] > (bignum - xmax) * rec) { /* Scale x by 1/(2*abs(x(j))). */ rec *= .5; dscal_(n, &rec, &x[1], &c__1); *scale *= rec; } } else if (xj * cnorm[j] > bignum - xmax) { /* Scale x by 1/2. */ dscal_(n, &c_b36, &x[1], &c__1); *scale *= .5; } if (upper) { if (j > 1) { /* Compute the update x(1:j-1) := x(1:j-1) - x(j) * A(1:j-1,j) */ i__3 = j - 1; d__1 = -x[j] * tscal; daxpy_(&i__3, &d__1, &ap[ip - j + 1], &c__1, &x[1], & c__1); i__3 = j - 1; i__ = idamax_(&i__3, &x[1], &c__1); xmax = (d__1 = x[i__], abs(d__1)); } ip -= j; } else { if (j < *n) { /* Compute the update x(j+1:n) := x(j+1:n) - x(j) * A(j+1:n,j) */ i__3 = *n - j; d__1 = -x[j] * tscal; daxpy_(&i__3, &d__1, &ap[ip + 1], &c__1, &x[j + 1], & c__1); i__3 = *n - j; i__ = j + idamax_(&i__3, &x[j + 1], &c__1); xmax = (d__1 = x[i__], abs(d__1)); } ip = ip + *n - j + 1; } /* L110: */ } } else { /* Solve A' * x = b */ ip = jfirst * (jfirst + 1) / 2; jlen = 1; i__2 = jlast; i__1 = jinc; for (j = jfirst; i__1 < 0 ? j >= i__2 : j <= i__2; j += i__1) { /* Compute x(j) = b(j) - sum A(k,j)*x(k). k<>j */ xj = (d__1 = x[j], abs(d__1)); uscal = tscal; rec = 1. / max(xmax,1.); if (cnorm[j] > (bignum - xj) * rec) { /* If x(j) could overflow, scale x by 1/(2*XMAX). */ rec *= .5; if (nounit) { tjjs = ap[ip] * tscal; } else { tjjs = tscal; } tjj = abs(tjjs); if (tjj > 1.) { /* Divide by A(j,j) when scaling x if A(j,j) > 1. Computing MIN */ d__1 = 1., d__2 = rec * tjj; rec = min(d__1,d__2); uscal /= tjjs; } if (rec < 1.) { dscal_(n, &rec, &x[1], &c__1); *scale *= rec; xmax *= rec; } } sumj = 0.; if (uscal == 1.) { /* If the scaling needed for A in the dot product is 1, call DDOT to perform the dot product. */ if (upper) { i__3 = j - 1; sumj = ddot_(&i__3, &ap[ip - j + 1], &c__1, &x[1], & c__1); } else if (j < *n) { i__3 = *n - j; sumj = ddot_(&i__3, &ap[ip + 1], &c__1, &x[j + 1], & c__1); } } else { /* Otherwise, use in-line code for the dot product. */ if (upper) { i__3 = j - 1; for (i__ = 1; i__ <= i__3; ++i__) { sumj += ap[ip - j + i__] * uscal * x[i__]; /* L120: */ } } else if (j < *n) { i__3 = *n - j; for (i__ = 1; i__ <= i__3; ++i__) { sumj += ap[ip + i__] * uscal * x[j + i__]; /* L130: */ } } } if (uscal == tscal) { /* Compute x(j) := ( x(j) - sumj ) / A(j,j) if 1/A(j,j) was not used to scale the dotproduct. */ x[j] -= sumj; xj = (d__1 = x[j], abs(d__1)); if (nounit) { /* Compute x(j) = x(j) / A(j,j), scaling if necessary. */ tjjs = ap[ip] * tscal; } else { tjjs = tscal; if (tscal == 1.) { goto L150; } } tjj = abs(tjjs); if (tjj > smlnum) { /* abs(A(j,j)) > SMLNUM: */ if (tjj < 1.) { if (xj > tjj * bignum) { /* Scale X by 1/abs(x(j)). */ rec = 1. / xj; dscal_(n, &rec, &x[1], &c__1); *scale *= rec; xmax *= rec; } } x[j] /= tjjs; } else if (tjj > 0.) { /* 0 < abs(A(j,j)) <= SMLNUM: */ if (xj > tjj * bignum) { /* Scale x by (1/abs(x(j)))*abs(A(j,j))*BIGNUM. */ rec = tjj * bignum / xj; dscal_(n, &rec, &x[1], &c__1); *scale *= rec; xmax *= rec; } x[j] /= tjjs; } else { /* A(j,j) = 0: Set x(1:n) = 0, x(j) = 1, and scale = 0, and compute a solution to A'*x = 0. */ i__3 = *n; for (i__ = 1; i__ <= i__3; ++i__) { x[i__] = 0.; /* L140: */ } x[j] = 1.; *scale = 0.; xmax = 0.; } L150: ; } else { /* Compute x(j) := x(j) / A(j,j) - sumj if the dot product has already been divided by 1/A(j,j). */ x[j] = x[j] / tjjs - sumj; } /* Computing MAX */ d__2 = xmax, d__3 = (d__1 = x[j], abs(d__1)); xmax = max(d__2,d__3); ++jlen; ip += jinc * jlen; /* L160: */ } } *scale /= tscal; } /* Scale the column norms by 1/TSCAL for return. */ if (tscal != 1.) { d__1 = 1. / tscal; dscal_(n, &d__1, &cnorm[1], &c__1); } return 0; /* End of DLATPS */ } /* dlatps_ */