subroutine sgbco(abd,lda,n,ml,mu,ipvt,rcond,z)
integer lda,n,ml,mu,ipvt(1)
real abd(lda,1),z(1)
real rcond
c
c sgbco factors a real band matrix by gaussian
c elimination and estimates the condition of the matrix.
c
c if rcond is not needed, sgbfa is slightly faster.
c to solve a*x = b , follow sgbco by sgbsl.
c to compute inverse(a)*c , follow sgbco by sgbsl.
c to compute determinant(a) , follow sgbco by sgbdi.
c
c on entry
c
c abd real(lda, n)
c contains the matrix in band storage. the columns
c of the matrix are stored in the columns of abd and
c the diagonals of the matrix are stored in rows
c ml+1 through 2*ml+mu+1 of abd .
c see the comments below for details.
c
c lda integer
c the leading dimension of the array abd .
c lda must be .ge. 2*ml + mu + 1 .
c
c n integer
c the order of the original matrix.
c
c ml integer
c number of diagonals below the main diagonal.
c 0 .le. ml .lt. n .
c
c mu integer
c number of diagonals above the main diagonal.
c 0 .le. mu .lt. n .
c more efficient if ml .le. mu .
c
c on return
c
c abd an upper triangular matrix in band storage and
c the multipliers which were used to obtain it.
c the factorization can be written a = l*u where
c l is a product of permutation and unit lower
c triangular matrices and u is upper triangular.
c
c ipvt integer(n)
c an integer vector of pivot indices.
c
c rcond real
c an estimate of the reciprocal condition of a .
c for the system a*x = b , relative perturbations
c in a and b of size epsilon may cause
c relative perturbations in x of size epsilon/rcond .
c if rcond is so small that the logical expression
c 1.0 + rcond .eq. 1.0
c is true, then a may be singular to working
c precision. in particular, rcond is zero if
c exact singularity is detected or the estimate
c underflows.
c
c z real(n)
c a work vector whose contents are usually unimportant.
c if a is close to a singular matrix, then z is
c an approximate null vector in the sense that
c norm(a*z) = rcond*norm(a)*norm(z) .
c
c band storage
c
c if a is a band matrix, the following program segment
c will set up the input.
c
c ml = (band width below the diagonal)
c mu = (band width above the diagonal)
c m = ml + mu + 1
c do 20 j = 1, n
c i1 = max0(1, j-mu)
c i2 = min0(n, j+ml)
c do 10 i = i1, i2
c k = i - j + m
c abd(k,j) = a(i,j)
c 10 continue
c 20 continue
c
c this uses rows ml+1 through 2*ml+mu+1 of abd .
c in addition, the first ml rows in abd are used for
c elements generated during the triangularization.
c the total number of rows needed in abd is 2*ml+mu+1 .
c the ml+mu by ml+mu upper left triangle and the
c ml by ml lower right triangle are not referenced.
c
c example.. if the original matrix is
c
c 11 12 13 0 0 0
c 21 22 23 24 0 0
c 0 32 33 34 35 0
c 0 0 43 44 45 46
c 0 0 0 54 55 56
c 0 0 0 0 65 66
c
c then n = 6, ml = 1, mu = 2, lda .ge. 5 and abd should contain
c
c * * * + + + , * = not used
c * * 13 24 35 46 , + = used for pivoting
c * 12 23 34 45 56
c 11 22 33 44 55 66
c 21 32 43 54 65 *
c
c linpack. this version dated 08/14/78 .
c cleve moler, university of new mexico, argonne national lab.
c
c subroutines and functions
c
c linpack sgbfa
c blas saxpy,sdot,sscal,sasum
c fortran abs,amax1,max0,min0,sign
c
c internal variables
c
real sdot,ek,t,wk,wkm
real anorm,s,sasum,sm,ynorm
integer is,info,j,ju,k,kb,kp1,l,la,lm,lz,m,mm
c
c
c compute 1-norm of a
c
anorm = 0.0e0
l = ml + 1
is = l + mu
do 10 j = 1, n
anorm = amax1(anorm,sasum(l,abd(is,j),1))
if (is .gt. ml + 1) is = is - 1
if (j .le. mu) l = l + 1
if (j .ge. n - ml) l = l - 1
10 continue
c
c factor
c
call sgbfa(abd,lda,n,ml,mu,ipvt,info)
c
c rcond = 1/(norm(a)*(estimate of norm(inverse(a)))) .
c estimate = norm(z)/norm(y) where a*z = y and trans(a)*y = e .
c trans(a) is the transpose of a . the components of e are
c chosen to cause maximum local growth in the elements of w where
c trans(u)*w = e . the vectors are frequently rescaled to avoid
c overflow.
c
c solve trans(u)*w = e
c
ek = 1.0e0
do 20 j = 1, n
z(j) = 0.0e0
20 continue
m = ml + mu + 1
ju = 0
do 100 k = 1, n
if (z(k) .ne. 0.0e0) ek = sign(ek,-z(k))
if (abs(ek-z(k)) .le. abs(abd(m,k))) go to 30
s = abs(abd(m,k))/abs(ek-z(k))
call sscal(n,s,z,1)
ek = s*ek
30 continue
wk = ek - z(k)
wkm = -ek - z(k)
s = abs(wk)
sm = abs(wkm)
if (abd(m,k) .eq. 0.0e0) go to 40
wk = wk/abd(m,k)
wkm = wkm/abd(m,k)
go to 50
40 continue
wk = 1.0e0
wkm = 1.0e0
50 continue
kp1 = k + 1
ju = min0(max0(ju,mu+ipvt(k)),n)
mm = m
if (kp1 .gt. ju) go to 90
do 60 j = kp1, ju
mm = mm - 1
sm = sm + abs(z(j)+wkm*abd(mm,j))
z(j) = z(j) + wk*abd(mm,j)
s = s + abs(z(j))
60 continue
if (s .ge. sm) go to 80
t = wkm - wk
wk = wkm
mm = m
do 70 j = kp1, ju
mm = mm - 1
z(j) = z(j) + t*abd(mm,j)
70 continue
80 continue
90 continue
z(k) = wk
100 continue
s = 1.0e0/sasum(n,z,1)
call sscal(n,s,z,1)
c
c solve trans(l)*y = w
c
do 120 kb = 1, n
k = n + 1 - kb
lm = min0(ml,n-k)
if (k .lt. n) z(k) = z(k) + sdot(lm,abd(m+1,k),1,z(k+1),1)
if (abs(z(k)) .le. 1.0e0) go to 110
s = 1.0e0/abs(z(k))
call sscal(n,s,z,1)
110 continue
l = ipvt(k)
t = z(l)
z(l) = z(k)
z(k) = t
120 continue
s = 1.0e0/sasum(n,z,1)
call sscal(n,s,z,1)
c
ynorm = 1.0e0
c
c solve l*v = y
c
do 140 k = 1, n
l = ipvt(k)
t = z(l)
z(l) = z(k)
z(k) = t
lm = min0(ml,n-k)
if (k .lt. n) call saxpy(lm,t,abd(m+1,k),1,z(k+1),1)
if (abs(z(k)) .le. 1.0e0) go to 130
s = 1.0e0/abs(z(k))
call sscal(n,s,z,1)
ynorm = s*ynorm
130 continue
140 continue
s = 1.0e0/sasum(n,z,1)
call sscal(n,s,z,1)
ynorm = s*ynorm
c
c solve u*z = w
c
do 160 kb = 1, n
k = n + 1 - kb
if (abs(z(k)) .le. abs(abd(m,k))) go to 150
s = abs(abd(m,k))/abs(z(k))
call sscal(n,s,z,1)
ynorm = s*ynorm
150 continue
if (abd(m,k) .ne. 0.0e0) z(k) = z(k)/abd(m,k)
if (abd(m,k) .eq. 0.0e0) z(k) = 1.0e0
lm = min0(k,m) - 1
la = m - lm
lz = k - lm
t = -z(k)
call saxpy(lm,t,abd(la,k),1,z(lz),1)
160 continue
c make znorm = 1.0
s = 1.0e0/sasum(n,z,1)
call sscal(n,s,z,1)
ynorm = s*ynorm
c
if (anorm .ne. 0.0e0) rcond = ynorm/anorm
if (anorm .eq. 0.0e0) rcond = 0.0e0
return
end