dlib C++ Library - optimization.h
// Copyright (C) 2008 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_OPTIMIZATIOn_H_
#define DLIB_OPTIMIZATIOn_H_
#include <cmath>
#include <limits>
#include "optimization_abstract.h"
#include "optimization_search_strategies.h"
#include "optimization_stop_strategies.h"
#include "optimization_line_search.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// Functions that transform other functions
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <typename funct>
class central_differences
{
public:
central_differences(const funct& f_, double eps_ = 1e-7) : f(f_), eps(eps_){}
template <typename T>
typename T::matrix_type operator()(const T& x) const
{
// T must be some sort of dlib matrix
COMPILE_TIME_ASSERT(is_matrix<T>::value);
typename T::matrix_type der(x.size());
typename T::matrix_type e(x);
for (long i = 0; i < x.size(); ++i)
{
const double old_val = e(i);
e(i) += eps;
const double delta_plus = f(e);
e(i) = old_val - eps;
const double delta_minus = f(e);
der(i) = (delta_plus - delta_minus)/((old_val+eps)-(old_val-eps));
// and finally restore the old value of this element
e(i) = old_val;
}
return der;
}
template <typename T, typename U>
typename U::matrix_type operator()(const T& item, const U& x) const
{
// U must be some sort of dlib matrix
COMPILE_TIME_ASSERT(is_matrix<U>::value);
typename U::matrix_type der(x.size());
typename U::matrix_type e(x);
for (long i = 0; i < x.size(); ++i)
{
const double old_val = e(i);
e(i) += eps;
const double delta_plus = f(item,e);
e(i) = old_val - eps;
const double delta_minus = f(item,e);
der(i) = (delta_plus - delta_minus)/((old_val+eps)-(old_val-eps));
// and finally restore the old value of this element
e(i) = old_val;
}
return der;
}
double operator()(const double& x) const
{
return (f(x+eps)-f(x-eps))/((x+eps)-(x-eps));
}
private:
const funct& f;
const double eps;
};
template <typename funct>
const central_differences<funct> derivative(const funct& f) { return central_differences<funct>(f); }
template <typename funct>
const central_differences<funct> derivative(const funct& f, double eps)
{
DLIB_ASSERT (
eps > 0,
"\tcentral_differences derivative(f,eps)"
<< "\n\tYou must give an epsilon > 0"
<< "\n\teps: " << eps
);
return central_differences<funct>(f,eps);
}
// ----------------------------------------------------------------------------------------
template <typename funct, typename EXP1, typename EXP2>
struct clamped_function_object
{
clamped_function_object(
const funct& f_,
const matrix_exp<EXP1>& x_lower_,
const matrix_exp<EXP2>& x_upper_
) : f(f_), x_lower(x_lower_), x_upper(x_upper_)
{
}
template <typename T>
double operator() (
const T& x
) const
{
return f(clamp(x,x_lower,x_upper));
}
const funct& f;
const matrix_exp<EXP1>& x_lower;
const matrix_exp<EXP2>& x_upper;
};
template <typename funct, typename EXP1, typename EXP2>
clamped_function_object<funct,EXP1,EXP2> clamp_function(
const funct& f,
const matrix_exp<EXP1>& x_lower,
const matrix_exp<EXP2>& x_upper
) { return clamped_function_object<funct,EXP1,EXP2>(f,x_lower,x_upper); }
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// Functions that perform unconstrained optimization
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <
typename search_strategy_type,
typename stop_strategy_type,
typename funct,
typename funct_der,
typename T
>
double find_min (
search_strategy_type search_strategy,
stop_strategy_type stop_strategy,
const funct& f,
const funct_der& der,
T& x,
double min_f
)
{
COMPILE_TIME_ASSERT(is_matrix<T>::value);
// The starting point (i.e. x) must be a column vector.
COMPILE_TIME_ASSERT(T::NC <= 1);
DLIB_CASSERT (
is_col_vector(x),
"\tdouble find_min()"
<< "\n\tYou have to supply column vectors to this function"
<< "\n\tx.nc(): " << x.nc()
);
T g, s;
double f_value = f(x);
g = der(x);
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
while(stop_strategy.should_continue_search(x, f_value, g) && f_value > min_f)
{
s = search_strategy.get_next_direction(x, f_value, g);
double alpha = line_search(
make_line_search_function(f,x,s, f_value),
f_value,
make_line_search_function(der,x,s, g),
dot(g,s), // compute initial gradient for the line search
search_strategy.get_wolfe_rho(), search_strategy.get_wolfe_sigma(), min_f,
search_strategy.get_max_line_search_iterations());
// Take the search step indicated by the above line search
x += alpha*s;
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
}
return f_value;
}
// ----------------------------------------------------------------------------------------
template <
typename search_strategy_type,
typename stop_strategy_type,
typename funct,
typename funct_der,
typename T
>
double find_max (
search_strategy_type search_strategy,
stop_strategy_type stop_strategy,
const funct& f,
const funct_der& der,
T& x,
double max_f
)
{
COMPILE_TIME_ASSERT(is_matrix<T>::value);
// The starting point (i.e. x) must be a column vector.
COMPILE_TIME_ASSERT(T::NC <= 1);
DLIB_CASSERT (
is_col_vector(x),
"\tdouble find_max()"
<< "\n\tYou have to supply column vectors to this function"
<< "\n\tx.nc(): " << x.nc()
);
T g, s;
// This function is basically just a copy of find_min() but with - put in the right places
// to flip things around so that it ends up looking for the max rather than the min.
double f_value = -f(x);
g = -der(x);
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
while(stop_strategy.should_continue_search(x, f_value, g) && f_value > -max_f)
{
s = search_strategy.get_next_direction(x, f_value, g);
double alpha = line_search(
negate_function(make_line_search_function(f,x,s, f_value)),
f_value,
negate_function(make_line_search_function(der,x,s, g)),
dot(g,s), // compute initial gradient for the line search
search_strategy.get_wolfe_rho(), search_strategy.get_wolfe_sigma(), -max_f,
search_strategy.get_max_line_search_iterations()
);
// Take the search step indicated by the above line search
x += alpha*s;
// Don't forget to negate these outputs from the line search since they are
// from the unnegated versions of f() and der()
g *= -1;
f_value *= -1;
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
// Gradient is zero, no more progress is possible. So stop.
if (alpha == 0)
break;
}
return -f_value;
}
// ----------------------------------------------------------------------------------------
template <
typename search_strategy_type,
typename stop_strategy_type,
typename funct,
typename T
>
double find_min_using_approximate_derivatives (
search_strategy_type search_strategy,
stop_strategy_type stop_strategy,
const funct& f,
T& x,
double min_f,
double derivative_eps = 1e-7
)
{
COMPILE_TIME_ASSERT(is_matrix<T>::value);
// The starting point (i.e. x) must be a column vector.
COMPILE_TIME_ASSERT(T::NC <= 1);
DLIB_CASSERT (
is_col_vector(x) && derivative_eps > 0,
"\tdouble find_min_using_approximate_derivatives()"
<< "\n\tYou have to supply column vectors to this function"
<< "\n\tx.nc(): " << x.nc()
<< "\n\tderivative_eps: " << derivative_eps
);
T g, s;
double f_value = f(x);
g = derivative(f,derivative_eps)(x);
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
while(stop_strategy.should_continue_search(x, f_value, g) && f_value > min_f)
{
s = search_strategy.get_next_direction(x, f_value, g);
double alpha = line_search(
make_line_search_function(f,x,s,f_value),
f_value,
derivative(make_line_search_function(f,x,s),derivative_eps),
dot(g,s), // Sometimes the following line is a better way of determining the initial gradient.
//derivative(make_line_search_function(f,x,s),derivative_eps)(0),
search_strategy.get_wolfe_rho(), search_strategy.get_wolfe_sigma(), min_f,
search_strategy.get_max_line_search_iterations()
);
// Take the search step indicated by the above line search
x += alpha*s;
g = derivative(f,derivative_eps)(x);
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
}
return f_value;
}
// ----------------------------------------------------------------------------------------
template <
typename search_strategy_type,
typename stop_strategy_type,
typename funct,
typename T
>
double find_max_using_approximate_derivatives (
search_strategy_type search_strategy,
stop_strategy_type stop_strategy,
const funct& f,
T& x,
double max_f,
double derivative_eps = 1e-7
)
{
COMPILE_TIME_ASSERT(is_matrix<T>::value);
// The starting point (i.e. x) must be a column vector.
COMPILE_TIME_ASSERT(T::NC <= 1);
DLIB_CASSERT (
is_col_vector(x) && derivative_eps > 0,
"\tdouble find_max_using_approximate_derivatives()"
<< "\n\tYou have to supply column vectors to this function"
<< "\n\tx.nc(): " << x.nc()
<< "\n\tderivative_eps: " << derivative_eps
);
// Just negate the necessary things and call the find_min version of this function.
return -find_min_using_approximate_derivatives(
search_strategy,
stop_strategy,
negate_function(f),
x,
-max_f,
derivative_eps
);
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// Functions for box constrained optimization
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <typename T, typename U, typename V>
T zero_bounded_variables (
const double eps,
T vect,
const T& x,
const T& gradient,
const U& x_lower,
const V& x_upper
)
{
for (long i = 0; i < gradient.size(); ++i)
{
const double tol = eps*std::abs(x(i));
// if x(i) is an active bound constraint
if (x_lower(i)+tol >= x(i) && gradient(i) > 0)
vect(i) = 0;
else if (x_upper(i)-tol <= x(i) && gradient(i) < 0)
vect(i) = 0;
}
return vect;
}
// ----------------------------------------------------------------------------------------
template <typename T, typename U, typename V>
T gap_step_assign_bounded_variables (
const double eps,
T vect,
const T& x,
const T& gradient,
const U& x_lower,
const V& x_upper
)
{
for (long i = 0; i < gradient.size(); ++i)
{
const double tol = eps*std::abs(x(i));
// If x(i) is an active bound constraint then we should set its search
// direction such that a single step along the direction either does nothing or
// closes the gap of size tol before hitting the bound exactly.
if (x_lower(i)+tol >= x(i) && gradient(i) > 0)
vect(i) = x_lower(i)-x(i);
else if (x_upper(i)-tol <= x(i) && gradient(i) < 0)
vect(i) = x_upper(i)-x(i);
}
return vect;
}
// ----------------------------------------------------------------------------------------
template <
typename search_strategy_type,
typename stop_strategy_type,
typename funct,
typename funct_der,
typename T,
typename EXP1,
typename EXP2
>
double find_min_box_constrained (
search_strategy_type search_strategy,
stop_strategy_type stop_strategy,
const funct& f,
const funct_der& der,
T& x,
const matrix_exp<EXP1>& x_lower,
const matrix_exp<EXP2>& x_upper
)
{
/*
The implementation of this function is more or less based on the discussion in
the paper Projected Newton-type Methods in Machine Learning by Mark Schmidt, et al.
*/
// make sure the requires clause is not violated
COMPILE_TIME_ASSERT(is_matrix<T>::value);
// The starting point (i.e. x) must be a column vector.
COMPILE_TIME_ASSERT(T::NC <= 1);
DLIB_CASSERT (
is_col_vector(x) && is_col_vector(x_lower) && is_col_vector(x_upper) &&
x.size() == x_lower.size() && x.size() == x_upper.size(),
"\tdouble find_min_box_constrained()"
<< "\n\t The inputs to this function must be equal length column vectors."
<< "\n\t is_col_vector(x): " << is_col_vector(x)
<< "\n\t is_col_vector(x_upper): " << is_col_vector(x_upper)
<< "\n\t is_col_vector(x_upper): " << is_col_vector(x_upper)
<< "\n\t x.size(): " << x.size()
<< "\n\t x_lower.size(): " << x_lower.size()
<< "\n\t x_upper.size(): " << x_upper.size()
);
DLIB_ASSERT (
min(x_upper-x_lower) >= 0,
"\tdouble find_min_box_constrained()"
<< "\n\t You have to supply proper box constraints to this function."
<< "\n\r min(x_upper-x_lower): " << min(x_upper-x_lower)
);
T g, s;
double f_value = f(x);
g = der(x);
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
// gap_eps determines how close we have to get to a bound constraint before we
// start basically dropping it from the optimization and consider it to be an
// active constraint.
const double gap_eps = 1e-8;
double last_alpha = 1;
while(stop_strategy.should_continue_search(x, f_value, g))
{
s = search_strategy.get_next_direction(x, f_value, zero_bounded_variables(gap_eps, g, x, g, x_lower, x_upper));
s = gap_step_assign_bounded_variables(gap_eps, s, x, g, x_lower, x_upper);
double alpha = backtracking_line_search(
make_line_search_function(clamp_function(f,x_lower,x_upper), x, s, f_value),
f_value,
dot(g,s), // compute gradient for the line search
last_alpha,
search_strategy.get_wolfe_rho(),
search_strategy.get_max_line_search_iterations());
// Do a trust region style thing for alpha. The idea is that if we take a
// small step then we are likely to take another small step. So we reuse the
// alpha from the last iteration unless the line search didn't shrink alpha at
// all, in that case, we start with a bigger alpha next time.
if (alpha == last_alpha)
last_alpha = std::min(last_alpha*10,1.0);
else
last_alpha = alpha;
// Take the search step indicated by the above line search
x = dlib::clamp(x + alpha*s, x_lower, x_upper);
g = der(x);
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
}
return f_value;
}
// ----------------------------------------------------------------------------------------
template <
typename search_strategy_type,
typename stop_strategy_type,
typename funct,
typename funct_der,
typename T
>
double find_min_box_constrained (
search_strategy_type search_strategy,
stop_strategy_type stop_strategy,
const funct& f,
const funct_der& der,
T& x,
double x_lower,
double x_upper
)
{
// The starting point (i.e. x) must be a column vector.
COMPILE_TIME_ASSERT(T::NC <= 1);
typedef typename T::type scalar_type;
return find_min_box_constrained(search_strategy,
stop_strategy,
f,
der,
x,
uniform_matrix<scalar_type>(x.size(),1,x_lower),
uniform_matrix<scalar_type>(x.size(),1,x_upper) );
}
// ----------------------------------------------------------------------------------------
template <
typename search_strategy_type,
typename stop_strategy_type,
typename funct,
typename funct_der,
typename T,
typename EXP1,
typename EXP2
>
double find_max_box_constrained (
search_strategy_type search_strategy,
stop_strategy_type stop_strategy,
const funct& f,
const funct_der& der,
T& x,
const matrix_exp<EXP1>& x_lower,
const matrix_exp<EXP2>& x_upper
)
{
// make sure the requires clause is not violated
COMPILE_TIME_ASSERT(is_matrix<T>::value);
// The starting point (i.e. x) must be a column vector.
COMPILE_TIME_ASSERT(T::NC <= 1);
DLIB_CASSERT (
is_col_vector(x) && is_col_vector(x_lower) && is_col_vector(x_upper) &&
x.size() == x_lower.size() && x.size() == x_upper.size(),
"\tdouble find_max_box_constrained()"
<< "\n\t The inputs to this function must be equal length column vectors."
<< "\n\t is_col_vector(x): " << is_col_vector(x)
<< "\n\t is_col_vector(x_upper): " << is_col_vector(x_upper)
<< "\n\t is_col_vector(x_upper): " << is_col_vector(x_upper)
<< "\n\t x.size(): " << x.size()
<< "\n\t x_lower.size(): " << x_lower.size()
<< "\n\t x_upper.size(): " << x_upper.size()
);
DLIB_ASSERT (
min(x_upper-x_lower) >= 0,
"\tdouble find_max_box_constrained()"
<< "\n\t You have to supply proper box constraints to this function."
<< "\n\r min(x_upper-x_lower): " << min(x_upper-x_lower)
);
// This function is basically just a copy of find_min_box_constrained() but with - put
// in the right places to flip things around so that it ends up looking for the max
// rather than the min.
T g, s;
double f_value = -f(x);
g = -der(x);
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
// gap_eps determines how close we have to get to a bound constraint before we
// start basically dropping it from the optimization and consider it to be an
// active constraint.
const double gap_eps = 1e-8;
double last_alpha = 1;
while(stop_strategy.should_continue_search(x, f_value, g))
{
s = search_strategy.get_next_direction(x, f_value, zero_bounded_variables(gap_eps, g, x, g, x_lower, x_upper));
s = gap_step_assign_bounded_variables(gap_eps, s, x, g, x_lower, x_upper);
double alpha = backtracking_line_search(
negate_function(make_line_search_function(clamp_function(f,x_lower,x_upper), x, s, f_value)),
f_value,
dot(g,s), // compute gradient for the line search
last_alpha,
search_strategy.get_wolfe_rho(),
search_strategy.get_max_line_search_iterations());
// Do a trust region style thing for alpha. The idea is that if we take a
// small step then we are likely to take another small step. So we reuse the
// alpha from the last iteration unless the line search didn't shrink alpha at
// all, in that case, we start with a bigger alpha next time.
if (alpha == last_alpha)
last_alpha = std::min(last_alpha*10,1.0);
else
last_alpha = alpha;
// Take the search step indicated by the above line search
x = dlib::clamp(x + alpha*s, x_lower, x_upper);
g = -der(x);
// Don't forget to negate the output from the line search since it is from the
// unnegated version of f()
f_value *= -1;
if (!is_finite(f_value))
throw error("The objective function generated non-finite outputs");
if (!is_finite(g))
throw error("The objective function generated non-finite outputs");
}
return -f_value;
}
// ----------------------------------------------------------------------------------------
template <
typename search_strategy_type,
typename stop_strategy_type,
typename funct,
typename funct_der,
typename T
>
double find_max_box_constrained (
search_strategy_type search_strategy,
stop_strategy_type stop_strategy,
const funct& f,
const funct_der& der,
T& x,
double x_lower,
double x_upper
)
{
// The starting point (i.e. x) must be a column vector.
COMPILE_TIME_ASSERT(T::NC <= 1);
typedef typename T::type scalar_type;
return find_max_box_constrained(search_strategy,
stop_strategy,
f,
der,
x,
uniform_matrix<scalar_type>(x.size(),1,x_lower),
uniform_matrix<scalar_type>(x.size(),1,x_upper) );
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_OPTIMIZATIOn_H_