cubiclinesearch.3.man

COOOL：CWP面向对象最优化库（CWP Object Oriented Optimization Library） COOOL是C++类的一个集合

CUBICLINESEARCH(derived)OPTIMIZATION ALGORITHM CUBICLINESEARCH(derived)

                                                     Jun  1 15:17
NAME
    CubicLineSearch

SYNOPSIS
    #include <CubicLineSearch.hh>

    class CubicLineSearch : public LineSearch

        \fIPublic members\fP
            CubicLineSearch(ObjectiveFunction*, int);
            CubicLineSearch(ObjectiveFunction*, int, double);
            ~CubicLineSearch();
            Model<double> search(Model<double>&, Vector<double>&,
            double, double);
            Model<long> search(Model<long>&, Vector<double>&,
            double, double);

DESCRIPTION
    CubicLineSearch()
    This class implements the efficient line search procedure 
    described in Dennis and Schbabel's book entitled "Numerical
    Methods for Unconstrained and Nonlinear Equations. The 
    objective is to perform a unidimensional search for a minimum
    point along a specified direction in a multidimensional 
    space.
    

DESCRIPTION
    Constructors: 
     CubicLineSearch(ObjectiveFunction*f, int iter, double delta)
      f: Defines a pointer to the objective function.
      iter: Maximum number of iterations
      delta: This parameter is not used by the line search 
         itself. Rather it is used in the numerical computation
         of the derivatives using centered differences. For 
         example the derivative of f(x) at the point x0 would be
         given by 
         (f(x0 - delta) - f(x0 + delta) / 2 * delta)
    Methods:
     Model search(Model<double>&model0, 
    Vector<double>& direction, double descent, 
    double lambda)
    Here:
     model0:  Initial model for the line search
     direction: Search direction
     descent: dor product of search direction and gradient 
     lambda: This parameter controls the accuraccy of the 
             line search. 
           Lambda = .25 is a good choice.
    The minimizer along the search direction is returned 
    by the function.
    

CAVEATS
    Should I say no caveats !? This procedure seems to be fairly 
    robust. It has worked for a fairly broad class of 
    problems from optimization of standard test functons 
    in optimization theory and to hard geophysical problems as 
    stacking power optimization and amplitude seismogram inversion.
    But we never know ...

DEFINED MACROS
    CUBIC_LINE_SEARCH_HH

INCLUDED FILES
    "LineSearch.hh"

SOURCE FILES
    CubicLineSearch.cc