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Commit a6fd6929 authored by Davis King's avatar Davis King
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Fixed spec

parent ec7a4af1
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Showing with 30 additions and 23 deletions
dlib/global_optimization/find_global_maximum_abstract.h
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......@@ -3,17 +3,21 @@
#undef DLIB_FiND_GLOBAL_MAXIMUM_ABSTRACT_hH_
#ifdef DLIB_FiND_GLOBAL_MAXIMUM_ABSTRACT_hH_
#include "upper_bound_function_abstract.h"
#include "global_function_search_abstract.h"
#include "../metaprogramming.h"
#include "../matrix.h"
#include <utility>
#include <chrono>
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <typename T>
template <
typename T
>
auto call_function_and_expand_args(
T&& f,
const matrix<double,0,1>& args
......@@ -34,7 +38,7 @@ namespace dlib
However, the signature of f() is allowed to vary. In particular, if f()
takes a matrix<double,0,1> as a single argument then this function simply
calls f(args). However, if f() takes double arguments then args is expanded
appropriately, e.g it calls one of the following as appropriate:
appropriately, i.e. it calls one of the following as appropriate:
f(args(0))
f(args(0),args(1))
...
......@@ -48,10 +52,13 @@ namespace dlib
{
/*!
WHAT THIS OBJECT REPRESENTS
This is a simple typed integer class used to strongly type the "max number
of function calls" argument to find_global_maximum().
!*/
max_function_calls() = default;
explicit max_function_calls(size_t max_calls) : max_calls(max_calls) {}
size_t max_calls = std::numeric_limits<size_t>::max();
......@@ -59,7 +66,7 @@ namespace dlib
// ----------------------------------------------------------------------------------------
const auto FOREVER = std::chrono::hours(24*356*290); // 290 years
const auto FOREVER = std::chrono::hours(24*356*290); // 290 years, basically forever
// ----------------------------------------------------------------------------------------
......@@ -92,23 +99,23 @@ namespace dlib
ensures
- This function performs global optimization on the set of given functions.
The goal is to maximize the following objective function:
functions[i](x)
subject to the constraints on x defined by specs[i].
max_{i,x_i}: functions[i](x_i)
subject to the constraints on x_i defined by specs[i].
Once found, the return value of find_global_maximum() is:
make_pair(i, function_evaluation(x,functions[i](x))).
make_pair(i, function_evaluation(x_i,functions[i](x_i))).
That is, we search for the settings of i and x that return the largest output
and return those settings.
- The search is performed using the global_function_search object. See its
documentation for details of the algorithm.
- We set the global_function_search::get_solver_epsilon() parameter to
solver_epsilon. Therefore, the search will only attempt to find a global
maximizer to at most solver_epsilon accuracy. Once a local minimizer is
found to that accuracy the search will focus entirely on finding another
minimizers elsewhere rather than on further improving the current local
optima found so far. That is, once a local minima is identified to about
solver_epsilon accuracy, the algorithm will spend all its time exploring the
function to find other local minima to investigate.
- find_global_maximum runs until one of the following is true:
maximizer to at most solver_epsilon accuracy. Once a local maximizer is
found to that accuracy the search will focus entirely on finding other maxima
elsewhere rather than on further improving the current local optima found so
far. That is, once a local maxima is identified to about solver_epsilon
accuracy, the algorithm will spend all its time exploring the functions to
find other local maxima to investigate.
- find_global_maximum() runs until one of the following is true:
- The total number of calls to the provided functions is == num.max_calls
- More than max_runtime time has elapsed since the start of this function.
!*/
......@@ -148,19 +155,19 @@ namespace dlib
subject to the constraints on x defined by function_spec(bound1,bound2,is_integer_variable).
Once found, the return value of find_global_maximum() is:
function_evaluation(x,f(x))).
That is, we search for the setting of x that return the largest output and
return those settings.
That is, we search for the setting of x that returns the largest output and
return that setting.
- The search is performed using the global_function_search object. See its
documentation for details of the algorithm.
- We set the global_function_search::get_solver_epsilon() parameter to
solver_epsilon. Therefore, the search will only attempt to find a global
maximizer to at most solver_epsilon accuracy. Once a local minimizer is
found to that accuracy the search will focus entirely on finding another
minimizers elsewhere rather than on further improving the current local
optima found so far. That is, once a local minima is identified to about
solver_epsilon accuracy, the algorithm will spend all its time exploring the
function to find other local minima to investigate.
- find_global_maximum runs until one of the following is true:
maximizer to at most solver_epsilon accuracy. Once a local maximizer is
found to that accuracy the search will focus entirely on finding other maxima
elsewhere rather than on further improving the current local optima found so
far. That is, once a local maxima is identified to about solver_epsilon
accuracy, the algorithm will spend all its time exploring the function to
find other local maxima to investigate.
- find_global_maximum() runs until one of the following is true:
- The total number of calls to f() is == num.max_calls
- More than max_runtime time has elapsed since the start of this function.
!*/
......
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