brent.h

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#ifndef TOON_BRENT_H
#define TOON_BRENT_H
#include "TooN.h"
#include "helpers.h"
#include <limits>
#include <cmath>
#include <cstdlib>
#include <iomanip>


namespace TooN
{
    using std::numeric_limits;

    template<class Functor, class Precision> Vector<2, Precision> brent_line_search(Precision a, Precision x, Precision b, Precision fx, const Functor& func, int maxiterations, Precision tolerance = sqrt(numeric_limits<Precision>::epsilon()), Precision epsilon = numeric_limits<Precision>::epsilon())
    {
        using std::min;
        using std::max;

        using std::abs;

        //The golden ratio:
        const Precision g = (3.0 - sqrt(5))/2;
        
        //The following points are tracked by the algorithm:
        //a, b bracket the interval
        // x   is the best value so far
        // w   second best point so far
        // v   third best point so far
        // These may not be unique.
        
        //The following points are used during iteration
        // u   the point currently being evaluated
        // xm   (a+b)/2
        
        //The updates are tracked as:
        //e is the distance moved last step, or current if golden section is used
        //d is the point moved in the current step
        
        Precision w=x, v=x, fw=fx, fv=fx;
        
        Precision d=0, e=0;
        int i=0;

        while(abs(b-a) > (abs(a) + abs(b)) * tolerance + epsilon && i < maxiterations)
        {
            i++;
            //The midpoint of the bracket
            const Precision xm = (a+b)/2;

            //Per-iteration tolerance 
            const Precision tol1 = abs(x)*tolerance + epsilon;

            //If we recently had an unhelpful step, then do
            //not attempt a parabolic fit. This prevents bad parabolic
            //fits spoiling the convergence. Also, do not attempt to fit if
            //there is not yet enough unique information in x, w, v.
            if(abs(e) > tol1 && w != v)
            {
                //Attempt a parabolic through the best 3 points. The pdata is shifted
                //so that x = 0 and f(x) = 0. The remaining parameters are:
                //
                // xw  = w'    = w-x
                // fxw = f'(w) = f(w) - f(x)
                //
                // etc:
                const Precision fxw = fw - fx;
                const Precision fxv = fv - fx;
                const Precision xw = w-x;
                const Precision xv = v-x;

                //The parabolic fit has only second and first order coefficients:
                //const Precision c1 = (fxv*xw - fxw*xv) / (xw*xv*(xv-xw));
                //const Precision c2 = (fxw*xv*xv - fxv*xw*xw) / (xw*xv*(xv-xw));
                
                //The minimum is at -.5*c2 / c1;
                //
                //This can be written as p/q where:
                const Precision p = fxv*xw*xw - fxw*xv*xv;
                const Precision q = 2*(fxv*xw - fxw*xv);

                //The minimum is at p/q. The minimum must lie within the bracket for it
                //to be accepted. 
                // Also, we want the step to be smaller than half the old one. So:

                if(q == 0 || x + p/q < a || x+p/q > b || abs(p/q) > abs(e/2))
                {
                    //Parabolic fit no good. Take a golden section step instead
                    //and reset d and e.
                    if(x > xm)
                        e = a-x;
                    else
                        e = b-x;

                    d = g*e;
                }
                else
                {
                    //Parabolic fit was good. Shift d and e
                    e = d;
                    d = p/q;
                }
            }
            else
            {
                //Don't attempt a parabolic fit. Take a golden section step
                //instead and reset d and e.
                if(x > xm)
                    e = a-x;
                else
                    e = b-x;

                d = g*e;
            }

            const Precision u = x+d;
            //Our one function evaluation per iteration
            const Precision fu = func(u);

            if(fu < fx)
            {
                //U is the best known point.

                //Update the bracket
                if(u > x)
                    a = x;
                else
                    b = x;

                //Shift v, w, x
                v=w; fv = fw;
                w=x; fw = fx;
                x=u; fx = fu;
            }
            else
            {
                //u is not the best known point. However, it is within the
                //bracket.
                if(u < x)
                    a = u;
                else
                    b = u;

                if(fu <= fw || w == x)
                {
                    //Here, u is the new second-best point
                    v = w; fv = fw;
                    w = u; fw = fu;
                }
                else if(fu <= fv || v==x || v == w)
                {
                    //Here, u is the new third-best point.
                    v = u; fv = fu;
                }
            }
        }

        return makeVector(x, fx);
    }
}
#endif