void frprmn(float p[], int n, float ftol, int *iter, float *fret,
float (*func)(float []), void (*dfunc)(float [], float []))
Given a starting point p[1..n], Fletcher-Reeves-Polak-Ribiere minimization is performed on a
function func, using its gradient as calculated by a routine dfunc. The convergence tolerance
on the function value is input as ftol. Returned quantities are p (the location of the minimum),
iter (the number of iterations that were performed), and fret (the minimum value of the
function). The routine linmin is called to perform line minimizations.
{
void linmin(float p[], float xi[], int n, float *fret,
float (*func)(float []));
int j,its;
float gg,gam,fp,dgg;
float *g,*h,*xi;
g=vector(1,n);
h=vector(1,n);
xi=vector(1,n);
fp=(*func)(p); Initializations.
(*dfunc)(p,xi);
for (j=1;j<=n;j++) {
g[j] = -xi[j];
xi[j]=h[j]=g[j];
}
for (its=1;its<=ITMAX;its++) { Loop over iterations.
*iter=its;
linmin(p,xi,n,fret,func); Next statement is the normal return:
if (2.0*fabs(*fret-fp) <= ftol*(fabs(*fret)+fabs(fp)+EPS)) {
FREEALL
return;
}
fp= *fret;
(*dfunc)(p,xi);
dgg=gg=0.0;
for (j=1;j<=n;j++) {
gg += g[j]*g[j];
/* dgg += xi[j]*xi[j]; */ This statement for Fletcher-Reeves.
dgg += (xi[j]+g[j])*xi[j]; This statement for Polak-Ribiere.
}
if (gg == 0.0) { Unlikely. If gradient is exactly zero then
FREEALL we are already done.
return;
}
gam=dgg/gg;
for (j=1;j<=n;j++) {
g[j] = -xi[j];
xi[j]=h[j]=g[j]+gam*h[j];
}
}
nrerror("Too many iterations in frprmn");
}
