int gaussjordan(Matrix a, Vector b)
{
double big, dum, pivinv, tmp;
static int *indxc, *indxr, *ipiv;
static int oldn = 0;
int i, icol = 0, irow = 0, j, k, l, ll;
int m, n, p;
g_assert(a != 0);
g_assert(b != 0);
n = MatrixSize1(a);
m = MatrixSize2(a);
p = Vectorlength(b);
g_assert(m == n);
g_assert(m == p);
if(n != oldn)
{
oldn = n;
if(oldn > 0)
{
g_free(indxc);
g_free(indxr);
g_free(ipiv);
}
if(n > 0)
{
indxc = g_new(int, n);
indxr = g_new(int, n);
ipiv = g_new(int, n);
}
VecArray newVecArrayfromMatrix(const Matrix M)
{
int i, j, m, n;
VecArray a;
g_assert(M);
n = MatrixSize1(M);
m = MatrixSize2(M);
a = newVecArray(n);
for(j = 0; j < n; j++)
{
a[j] = newVector(m);
for(i = 0; i < m; i++) a[j][i] = M[j][i];
}
return a;
}
/* reduce symmetric matrix to tridiagonal form
*
* From numerical recipes tred2
*/
VecArray tridiagfromMatrix(Matrix a)
{
int n, i, j, k, l;
double scale, hh, h, g, f;
VecArray out;
Vector d, e;
g_assert(Matrixissquare(a));
n = MatrixSize1(a);
out = newpopulatedVecArray(2, n);
d = out[0];
e = out[1];
for(i = n-1; i > 0; i--)
{
l = i-1;
h = scale = 0.0;
if(l > 0)
{
for(k = 0; k <= l; k++)
scale += fabs(a[i][k]);
if(scale == 0.0)
e[i] = a[i][i];
else
{
for(k = 0; k <= l; k++)
{
a[i][k] /= scale;
h += a[i][k]*a[i][k];
}
f = a[i][i];
g = (f >= 0.0 ? -sqrt(h) : sqrt(h));
e[i] = scale*g;
h -= f*g;
a[i][l] = f - g;
f = 0.0;
for(j = 0; j <= l; j++)
{
a[j][i] = a[i][j]/h;
g = 0.0;
for(k = 0; k <= j; k++)
g += a[j][k]*a[i][k];
for(k = j+1; k <= l; k++)
g += a[k][j]*a[i][k];
e[j] = g/h;
f += e[j]*a[i][j];
}
hh = 0.5*f/h;
for(j = 0; j <= l; j++)
{
f=a[i][j];
e[j] = g = e[j] - hh*f;
for(k = 0; k <= j; k++)
a[j][k] -= (f*e[k] + g*a[i][k]);
}
}
}
else e[i] = a[i][l];
d[i] = h;
}
d[0] = e[0] = 0.0;
for(i = 0; i < n; i++)
{
l = i - 1;
if(i)
{
for(j = 0; j <= l; j++)
{
g = 0.0;
for(k = 0; k <= l; k++)
g += a[i][k]*a[k][j];
for(k = 1; k <= l; k++)
a[k][j] -= g*a[k][i];
}
}
d[i] = a[i][i];
a[i][i] = 1.0;
for(j = 0; j <= l;j++) a[j][i] = a[i][j] = 0.0;
}
return out;
}
