void MultiNewton::step(doublereal* x, doublereal* step,
OneDim& r, MultiJac& jac, int loglevel)
{
size_t iok;
size_t sz = r.size();
r.eval(npos, x, step);
#undef DEBUG_STEP
#ifdef DEBUG_STEP
vector_fp ssave(sz, 0.0);
for (size_t n = 0; n < sz; n++) {
step[n] = -step[n];
ssave[n] = step[n];
}
#else
for (size_t n = 0; n < sz; n++) {
step[n] = -step[n];
}
#endif
iok = jac.solve(step, step);
// if iok is non-zero, then solve failed
if (iok != 0) {
iok--;
size_t nd = r.nDomains();
size_t n;
for (n = nd-1; n != npos; n--)
if (iok >= r.start(n)) {
break;
}
Domain1D& dom = r.domain(n);
size_t offset = iok - r.start(n);
size_t pt = offset/dom.nComponents();
size_t comp = offset - pt*dom.nComponents();
throw CanteraError("MultiNewton::step",
"Jacobian is singular for domain "+
dom.id() + ", component "
+dom.componentName(comp)+" at point "
+int2str(pt)+"\n(Matrix row "
+int2str(iok)+") \nsee file bandmatrix.csv\n");
} else if (int(iok) < 0)
throw CanteraError("MultiNewton::step",
"iok = "+int2str(iok));
#ifdef DEBUG_STEP
bool ok = false;
Domain1D* d;
if (!ok) {
for (size_t n = 0; n < sz; n++) {
d = r.pointDomain(n);
int nvd = d->nComponents();
int pt = (n - d->loc())/nvd;
cout << "step: " << pt << " " <<
r.pointDomain(n)->componentName(n - d->loc() - nvd*pt)
<< " " << x[n] << " " << ssave[n] << " " << step[n] << endl;
}
}
#endif
}
/**
* Return the factor by which the undamped Newton step 'step0'
* must be multiplied in order to keep all solution components in
* all domains between their specified lower and upper bounds.
*/
doublereal MultiNewton::boundStep(const doublereal* x0,
const doublereal* step0, const OneDim& r, int loglevel) {
doublereal fbound = 1.0;
for (size_t i = 0; i < r.nDomains(); i++) {
fbound = fminn(fbound,
bound_step(x0 + r.start(i), step0 + r.start(i),
r.domain(i), loglevel));
}
return fbound;
}
/**
* Compute the weighted 2-norm of 'step'.
*/
doublereal MultiNewton::norm2(const doublereal* x,
const doublereal* step, OneDim& r) const {
doublereal f, sum = 0.0;//, fmx = 0.0;
size_t nd = r.nDomains();
for (size_t n = 0; n < nd; n++) {
f = norm_square(x + r.start(n), step + r.start(n),
r.domain(n));
sum += f;
}
sum /= r.size();
return sqrt(sum);
}
