dlib C++ Library - optimization_line_search_abstract.h

    // Copyright (C) 2008  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_OPTIMIZATIOn_ABSTRACT_
    #ifdef DLIB_OPTIMIZATIOn_ABSTRACT_

    #include <cmath>
    #include <limits>
    #include "../matrix/matrix_abstract.h"
    #include "../algs.h"

    namespace dlib
    {

    // ----------------------------------------------------------------------------------------

        template <
            typename funct,
            typename T
            >
        class line_search_funct;
        /*!
            This object is a function object that represents a line search function.

            Moreover, it represents a function with the signature:
                double l(double x)
        !*/

        template <
            typename funct,
            typename T
            >
        const line_search_funct<funct,T> make_line_search_function (
            const funct& f,
            const T& start,
            const T& direction
        );
        /*!
            requires
                - is_col_vector(start) && is_col_vector(direction) && start.size() == direction.size()
                  (i.e. start and direction should be column vectors of the same size)
                - f must return either a double or a column vector the same length as start
                - f(start + 1.5*direction) should be a valid expression
            ensures
                - if (f returns a double) then
                    - returns a line search function that computes l(x) == f(start + x*direction)
                - else
                    - returns a line search function that computes l(x) == dot(f(start + x*direction),direction).
                      That is, we assume f is the derivative of some other function and that what
                      f returns is a gradient vector.
                      So the following two expressions both create the derivative of l(x):
                        - derivative(make_line_search_function(funct,start,direction))
                        - make_line_search_function(derivative(funct),start,direction)
        !*/

        template <
            typename funct,
            typename T
            >
        const line_search_funct<funct,T> make_line_search_function (
            const funct& f,
            const T& start,
            const T& direction,
            double& f_out
        );
        /*!
            This function is identical to the above three argument version of make_line_search_function()
            except that, if f() outputs a double, every time f() is evaluated its output is also stored
            into f_out.
        !*/

        template <
            typename funct,
            typename T
            >
        const line_search_funct<funct,T> make_line_search_function (
            const funct& f,
            const T& start,
            const T& direction,
            T& gradient_out
        );
        /*!
            This function is identical to the above three argument version of make_line_search_function()
            except that, if f() outputs a column vector, every time f() is evaluated its output is also
            stored into gradient_out.
        !*/

    // ----------------------------------------------------------------------------------------

        inline double poly_min_extrap (
            double f0,
            double d0,
            double f1,
            double d1,
            double limit = 1
        );
        /*!
            ensures
                - let c(x) be a 3rd degree polynomial such that:
                    - c(0) == f0
                    - c(1) == f1
                    - derivative of c(x) at x==0 is d0
                    - derivative of c(x) at x==1 is d1
                - returns the point in the range [0,limit] that minimizes the polynomial c(x)
        !*/

    // ----------------------------------------------------------------------------------------

        inline double poly_min_extrap (
            double f0,
            double d0,
            double f1
        );
        /*!
            ensures
                - let c(x) be a 2nd degree polynomial such that:
                    - c(0) == f0
                    - c(1) == f1
                    - derivative of c(x) at x==0 is d0
                - returns the point in the range [0,1] that minimizes the polynomial c(x)
        !*/

    // ----------------------------------------------------------------------------------------

        inline double poly_min_extrap (
            double f0,
            double d0,
            double x1,
            double f_x1,
            double x2,
            double f_x2
        )
        /*!
            requires
                - 0 < x1 < x2
            ensures
                - let f(x) be a 3rd degree polynomial such that:
                    - f(0) == f0
                    - derivative of f(x) at x==0 is d0
                    - f(x1) == f_x1
                    - f(x2) == f_x2
                - returns the point in the range [0,x2] that minimizes the polynomial f(x)
        !*/

    // ----------------------------------------------------------------------------------------

        inline double lagrange_poly_min_extrap (
            double p1,
            double p2,
            double p3,
            double f1,
            double f2,
            double f3
        );
        /*!
            requires
                - f1 >= f2 <= f3
                - p1 < p2 < p3
            ensures
                - let c(x) be the second order Lagrange polynomial that interpolates the
                  points p1, p2, and p3 where c(p1)==f1, c(p2)==f2, and c(p3)==f3
                - this function returns the point in the range [p1,p3] that minimizes
                  the polynomial c(x)
        !*/

    // ----------------------------------------------------------------------------------------

        template <
            typename funct,
            typename funct_der
            >
        double line_search (
            const funct& f,
            const double f0,
            const funct_der& der,
            const double d0,
            double rho,
            double sigma,
            double min_f,
            unsigned long max_iter
        )
        /*!
            requires
                - 0 < rho < sigma < 1
                - f and der are scalar functions of scalars
                  (e.g. line_search_funct objects)
                - der is the derivative of f
                - f0 == f(0)
                - d0 == der(0)
                - max_iter > 0
            ensures
                - Performs a line search and uses the strong Wolfe conditions to decide when
                  the search can stop.
                    - rho == the parameter of the Wolfe sufficient decrease condition
                    - sigma == the parameter of the Wolfe curvature condition
                    - max_iter == the maximum number of iterations allowable.  After this
                      many evaluations of f() line_search() is guaranteed to terminate.
                - returns a value alpha such that f(alpha) is significantly closer to
                  the minimum of f than f(0).
                - It is assumed that the minimum possible value of f(x) is min_f.  So if
                  an alpha is found such that f(alpha) <= min_f then the search stops
                  immediately.
                - This function is also optimized for the case where der(0) is negative.  I.e.
                  positive values of the argument to f() should be in a descent direction.
                - When this function makes calls to f() and der() it always does so by
                  first calling f() and then calling der().  That is, these two functions
                  are always called in pairs with f() being called first and then der()
                  being called second.
        !*/

        /*
            A good discussion of the Wolfe conditions and line search algorithms in
            general can be found in the book Practical Methods of Optimization by R. Fletcher
            and also in the more recent book Numerical Optimization by Nocedal and Wright.
        */

    // ----------------------------------------------------------------------------------------

        template <
            typename funct
            >
        double backtracking_line_search (
            const funct& f,
            double f0,
            double d0,
            double alpha,
            double rho,
            unsigned long max_iter
        );
        /*!
            requires
                - 0 < rho < 1
                - f is a scalar function of scalars
                  (e.g. a line_search_funct object)
                - f0 == f(0)
                - d0 == the derivative of f() at f(0).
                - max_iter > 0
            ensures
                - Performs a backtracking line search and uses the Armijo sufficient decrease
                  rule to decide when the search can stop.
                    - rho == the parameter of the sufficient decrease condition.
                    - max_iter == the maximum number of iterations allowable.  After this many
                      evaluations of f() backtracking_line_search() is guaranteed to terminate.
                - The line search starts with the input alpha value and then backtracks until
                  it finds a good enough alpha value.  Once found, it returns the alpha value
                  such that f(alpha) is significantly closer to the minimum of f than f(0).
                - The returned value of alpha will always be the last value of alpha which was
                  passed to f().  That is, it will always be the case that the last call to f()
                  made by backtracking_line_search() was f(alpha) where alpha is the return
                  value from this function.
        !*/

    // ----------------------------------------------------------------------------------------

        template <
            typename funct
            >
        const negate_function_object<funct> negate_function(
            const funct& f
        );
        /*!
            requires
                - f == a function that returns a scalar
            ensures
                - returns a function that represents the negation of f.  That is,
                  the returned function object represents g(x) == -f(x)
        !*/

    // ----------------------------------------------------------------------------------------

        class optimize_single_variable_failure : public error;
        /*!
            This is the exception class used by the functions defined below.
        !*/

    // ----------------------------------------------------------------------------------------

        template <
            typename funct
            >
        double find_min_single_variable (
            const funct& f,
            double& starting_point,
            const double begin = -1e200,
            const double end = 1e200,
            const double eps = 1e-3,
            const long max_iter = 100,
            const double initial_search_radius = 1
        )
        /*!
            requires
                - eps > 0
                - max_iter > 1
                - begin <= starting_point <= end
                - f must be a function of a double that returns a double
                  (e.g. f(starting_point) should be a valid expression that evaluates to a double)
                - initial_search_radius > 0
            ensures
                - Finds a point P such that:
                    - P is a local minimum of the function f().
                    - begin <= P <= end
                - Evaluates f() no more than max_iter times
                - Stops searching when the window around the minimum point is smaller than eps.
                  The search will begin with the given starting_point and expand out to the
                  left and right by initial_search_radius sized steps.  So if you think the
                  minimum is likely to be found within X distance from the starting_point then
                  set initial_search_radius to X.
                - #starting_point == P
                - returns f(P)
            throws
                - optimize_single_variable_failure
                    This exception is thrown if max_iter iterations are performed without
                    determining the min point to the requested accuracy of eps.
        !*/

    // ----------------------------------------------------------------------------------------

        template <
            typename funct
            >
        double find_max_single_variable (
            const funct& f,
            double& starting_point,
            const double begin = -1e200,
            const double end = 1e200,
            const double eps = 1e-3,
            const long max_iter = 100,
            const double initial_search_radius = 1
        )
        /*!
            requires
                - eps > 0
                - max_iter > 1
                - begin <= starting_point <= end
                - f must be a function of a double that returns a double
                  (e.g. f(starting_point) should be a valid expression that evaluates to a double)
                - initial_search_radius > 0
            ensures
                - Finds a point P such that:
                    - P is a local maximum of the function f().
                    - begin <= P <= end
                - Evaluates f() no more than max_iter times
                - Stops searching when the window around the maximum point is smaller than eps.
                  The search will begin with the given starting_point and expand out to the
                  left and right by initial_search_radius sized steps.  So if you think the
                  maximum is likely to be found within X distance from the starting_point then
                  set initial_search_radius to X.
                - #starting_point == P
                - returns f(P)
            throws
                - optimize_single_variable_failure
                    This exception is thrown if max_iter iterations are performed without
                    determining the max point to the requested accuracy of eps.
        !*/

    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_OPTIMIZATIOn_ABSTRACT_