Removed a bunch of checks that prevented users from using references to

functions with the optimization code and forced the use of function pointers.
This was to avoid triggering a bug in gcc 4.0.  Since that compiler is no
longer officially supported by dlib I've removed these checks to increase
usability.

dlib/optimization/optimization.h

@@ -23,13 +23,6 @@ namespace dlib
     class central_differences
     {
     public:
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         central_differences(const funct& f_, double eps_ = 1e-7) : f(f_), eps(eps_){}
 
         template <typename T>
@@ -100,13 +93,6 @@ namespace dlib
     template <typename funct>
     const central_differences<funct> derivative(const funct& f, double eps) 
     { 
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         DLIB_ASSERT (
             eps > 0,
             "\tcentral_differences derivative(f,eps)"
@@ -122,13 +108,6 @@ namespace dlib
     class negate_function_object 
     {
     public:
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         negate_function_object(const funct& f_) : f(f_){}
 
         template <typename T>
@@ -201,14 +180,6 @@ namespace dlib
         double min_f
     )
     {
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-        COMPILE_TIME_ASSERT(is_function<funct_der>::value == false);
-
         COMPILE_TIME_ASSERT(is_matrix<T>::value);
         DLIB_ASSERT (
             is_col_vector(x),
@@ -266,14 +237,6 @@ namespace dlib
         double max_f
     )
     {
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-        COMPILE_TIME_ASSERT(is_function<funct_der>::value == false);
-
         COMPILE_TIME_ASSERT(is_matrix<T>::value);
         DLIB_ASSERT (
             is_col_vector(x),
@@ -338,13 +301,6 @@ namespace dlib
         double derivative_eps = 1e-7
     )
     {
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         COMPILE_TIME_ASSERT(is_matrix<T>::value);
         DLIB_ASSERT (
             is_col_vector(x) && derivative_eps > 0,
@@ -405,13 +361,6 @@ namespace dlib
         double derivative_eps = 1e-7
     )
     {
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         COMPILE_TIME_ASSERT(is_matrix<T>::value);
         DLIB_ASSERT (
             is_col_vector(x) && derivative_eps > 0,

dlib/optimization/optimization_bobyqa.h

@@ -3353,14 +3353,6 @@ L210:
         const long max_f_evals
     ) 
     {
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
-
         // check the requirements.  Also split the assert up so that the error message isn't huge.
         DLIB_CASSERT(is_col_vector(x) && is_col_vector(x_lower) && is_col_vector(x_upper) &&
                     x.size() == x_lower.size() && x_lower.size() == x_upper.size() &&

dlib/optimization/optimization_line_search.h

@@ -19,13 +19,6 @@ namespace dlib
     class line_search_funct 
     {
     public:
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         line_search_funct(const funct& f_, const T& start_, const T& direction_) 
             : f(f_),start(start_), direction(direction_), matrix_r(0), scalar_r(0)
         {}
@@ -77,13 +70,6 @@ namespace dlib
     template <typename funct, typename T>
     const line_search_funct<funct,T> make_line_search_function(const funct& f, const T& start, const T& direction) 
     { 
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         COMPILE_TIME_ASSERT(is_matrix<T>::value);
         DLIB_ASSERT (
             is_col_vector(start) && is_col_vector(direction) && start.size() == direction.size(),
@@ -102,13 +88,6 @@ namespace dlib
     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) 
     { 
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         COMPILE_TIME_ASSERT(is_matrix<T>::value);
         DLIB_ASSERT (
             is_col_vector(start) && is_col_vector(direction) && start.size() == direction.size(),
@@ -127,13 +106,6 @@ namespace dlib
     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& grad_out) 
     { 
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         COMPILE_TIME_ASSERT(is_matrix<T>::value);
         DLIB_ASSERT (
             is_col_vector(start) && is_col_vector(direction) && start.size() == direction.size(),
@@ -269,14 +241,6 @@ namespace dlib
         unsigned long max_iter 
     )
     {
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-        COMPILE_TIME_ASSERT(is_function<funct_der>::value == false);
-
         DLIB_ASSERT (
             0 < rho && rho < sigma && sigma < 1 && max_iter > 0,
             "\tdouble line_search()"
@@ -529,13 +493,6 @@ namespace dlib
         const long max_iter = 100
     )
     {
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         DLIB_CASSERT( eps > 0 &&
                       max_iter > 1 &&
                       begin <= starting_point && starting_point <= end,
@@ -790,13 +747,6 @@ namespace dlib
         const long max_iter = 100
     )
     {
-        // You get an error on this line when you pass in a global function to this function.
-        // You have to either use a function object or pass a pointer to your global function
-        // by taking its address using the & operator.  (This check is here because gcc 4.0
-        // has a bug that causes it to silently corrupt return values from functions that
-        // invoked through a reference)
-        COMPILE_TIME_ASSERT(is_function<funct>::value == false);
-
         return -find_min_single_variable(negate_function(f), starting_point, begin, end, eps, max_iter);
     }
 

dlib/optimization/optimization_search_strategies.h

@@ -313,7 +313,7 @@ namespace dlib
 
     template <typename hessian_funct>
     newton_search_strategy_obj<hessian_funct> newton_search_strategy (
-        const hessian_funct& hessian
+        hessian_funct hessian
     ) { return newton_search_strategy_obj<hessian_funct>(hessian); }
 
 // ----------------------------------------------------------------------------------------

dlib/optimization/optimization_search_strategies_abstract.h

@@ -313,7 +313,7 @@ namespace dlib
 
     template <typename hessian_funct>
     newton_search_strategy_obj<hessian_funct> newton_search_strategy (
-        const hessian_funct& hessian
+        hessian_funct hessian
     ) { return newton_search_strategy_obj<hessian_funct>(hessian); }
     /*!
         ensures

dlib/test/least_squares.cpp

@@ -35,8 +35,8 @@ namespace
             ch = chebyquad_start(2);
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -53,8 +53,8 @@ namespace
             ch = chebyquad_start(2);
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -73,8 +73,8 @@ namespace
             ch = chebyquad_start(2);
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -92,8 +92,8 @@ namespace
             ch = chebyquad_start(2);
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -112,8 +112,8 @@ namespace
             ch = chebyquad_start(4);
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -131,8 +131,8 @@ namespace
             ch = chebyquad_start(4);
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -152,8 +152,8 @@ namespace
             ch = chebyquad_start(6);
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -174,8 +174,8 @@ namespace
             ch = chebyquad_start(6);
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -195,8 +195,8 @@ namespace
             ch = chebyquad_start(8);
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -214,8 +214,8 @@ namespace
             ch = chebyquad_start(8);
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &chebyquad_residual,
-                                derivative(&chebyquad_residual),
+                                chebyquad_residual,
+                                derivative(chebyquad_residual),
                                 range(0,ch.size()-1),
                                 ch);
 
@@ -239,8 +239,8 @@ namespace
             ch = brown_start();
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 300),
-                                &brown_residual,
-                                derivative(&brown_residual),
+                                brown_residual,
+                                derivative(brown_residual),
                                 range(1,20),
                                 ch);
 
@@ -258,8 +258,8 @@ namespace
             ch = brown_start();
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &brown_residual,
-                                derivative(&brown_residual),
+                                brown_residual,
+                                derivative(brown_residual),
                                 range(1,20),
                                 ch);
 
@@ -301,8 +301,8 @@ namespace
             ch = rosen_start<double>();
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &rosen_residual_double,
-                                &rosen_residual_derivative_double,
+                                rosen_residual_double,
+                                rosen_residual_derivative_double,
                                 range(1,20),
                                 ch);
 
@@ -319,8 +319,8 @@ namespace
             ch = rosen_start<double>();
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &rosen_residual_double,
-                                &rosen_residual_derivative_double,
+                                rosen_residual_double,
+                                rosen_residual_derivative_double,
                                 range(1,20),
                                 ch);
 
@@ -340,8 +340,8 @@ namespace
             ch = rosen_start<double>();
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &rosen_residual_double,
-                                derivative(&rosen_residual_double),
+                                rosen_residual_double,
+                                derivative(rosen_residual_double),
                                 range(1,20),
                                 ch);
 
@@ -358,8 +358,8 @@ namespace
             ch = rosen_start<float>();
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &rosen_residual_float,
-                                derivative(&rosen_residual_float),
+                                rosen_residual_float,
+                                derivative(rosen_residual_float),
                                 range(1,20),
                                 ch);
 
@@ -376,8 +376,8 @@ namespace
             ch = rosen_start<float>();
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &rosen_residual_float,
-                                derivative(&rosen_residual_float),
+                                rosen_residual_float,
+                                derivative(rosen_residual_float),
                                 range(1,20),
                                 ch);
 
@@ -394,8 +394,8 @@ namespace
             ch = rosen_start<double>();
 
             solve_least_squares_lm(objective_delta_stop_strategy(1e-13, 80),
-                                &rosen_residual_double,
-                                derivative(&rosen_residual_double),
+                                rosen_residual_double,
+                                derivative(rosen_residual_double),
                                 range(1,20),
                                 ch);
 
@@ -412,8 +412,8 @@ namespace
             ch = rosen_big_start<double>();
 
             solve_least_squares(objective_delta_stop_strategy(1e-13, 80),
-                                &rosen_big_residual_double,
-                                derivative(&rosen_big_residual_double),
+                                rosen_big_residual_double,
+                                derivative(rosen_big_residual_double),
                                 range(1,2),
                                 ch);
 

examples/least_squares_ex.cpp

@@ -111,7 +111,7 @@ int main()
         // the approximate derivative computed using central differences (via derivative()).  
         // If this value is big then it means we probably typed the derivative function incorrectly.
         cout << "derivative error: " << length(residual_derivative(data_samples[0], params) - 
-                                               derivative(&residual)(data_samples[0], params) ) << endl;
+                                               derivative(residual)(data_samples[0], params) ) << endl;
 
 
 
@@ -126,8 +126,8 @@ int main()
         // Use the Levenberg-Marquardt method to determine the parameters which
         // minimize the sum of all squared residuals.
         solve_least_squares_lm(objective_delta_stop_strategy(1e-7).be_verbose(), 
-                               &residual,
-                               &residual_derivative,
+                               residual,
+                               residual_derivative,
                                data_samples,
                                x);
 
@@ -144,8 +144,8 @@ int main()
         // If we didn't create the residual_derivative function then we could
         // have used this method which numerically approximates the derivatives for you.
         solve_least_squares_lm(objective_delta_stop_strategy(1e-7).be_verbose(), 
-                               &residual,
-                               derivative(&residual),
+                               residual,
+                               derivative(residual),
                                data_samples,
                                x);
 
@@ -163,8 +163,8 @@ int main()
         // where the residuals don't go to zero at the solution.  So in these cases
         // it may provide a better answer.
         solve_least_squares(objective_delta_stop_strategy(1e-7).be_verbose(), 
-                            &residual,
-                            &residual_derivative,
+                            residual,
+                            residual_derivative,
                             data_samples,
                             x);
 

examples/optimization_ex.cpp

@@ -175,7 +175,7 @@ int main()
         // the results are similar.  If they are very different then you probably made a 
         // mistake.  So the first thing we do is compare the results at a test point: 
         cout << "Difference between analytic derivative and numerical approximation of derivative: " 
-              << length(derivative(&rosen)(starting_point) - rosen_derivative(starting_point)) << endl;
+              << length(derivative(rosen)(starting_point) - rosen_derivative(starting_point)) << endl;
 
 
         cout << "Find the minimum of the rosen function()" << endl;
@@ -194,7 +194,7 @@ int main()
 
         find_min(bfgs_search_strategy(),  // Use BFGS search algorithm
                  objective_delta_stop_strategy(1e-7), // Stop when the change in rosen() is less than 1e-7
-                 &rosen, &rosen_derivative, starting_point, -1);
+                 rosen, rosen_derivative, starting_point, -1);
         // Once the function ends the starting_point vector will contain the optimum point 
         // of (1,1).
         cout << "rosen solution:\n" << starting_point << endl;
@@ -207,7 +207,7 @@ int main()
         starting_point = -94, 5.2;
         find_min_using_approximate_derivatives(bfgs_search_strategy(),
                                                objective_delta_stop_strategy(1e-7),
-                                               &rosen, starting_point, -1);
+                                               rosen, starting_point, -1);
         // Again the correct minimum point is found and stored in starting_point
         cout << "rosen solution:\n" << starting_point << endl;
 
@@ -222,14 +222,14 @@ int main()
         find_min(lbfgs_search_strategy(10),  // The 10 here is basically a measure of how much memory L-BFGS will use.
                  objective_delta_stop_strategy(1e-7).be_verbose(),  // Adding be_verbose() causes a message to be 
                                                                     // printed for each iteration of optimization.
-                 &rosen, &rosen_derivative, starting_point, -1);
+                 rosen, rosen_derivative, starting_point, -1);
 
         cout << endl << "rosen solution: \n" << starting_point << endl;
 
         starting_point = -94, 5.2;
         find_min_using_approximate_derivatives(lbfgs_search_strategy(10),
                                                objective_delta_stop_strategy(1e-7),
-                                               &rosen, starting_point, -1);
+                                               rosen, starting_point, -1);
         cout << "rosen solution: \n"<< starting_point << endl;
 
 
@@ -237,10 +237,10 @@ int main()
         // In many cases, it is useful if we also provide second derivative information
         // to the optimizers.  Two examples of how we can do that are shown below.  
         starting_point = 0.8, 1.3;
-        find_min(newton_search_strategy(&rosen_hessian),
+        find_min(newton_search_strategy(rosen_hessian),
                  objective_delta_stop_strategy(1e-7),
-                 &rosen,
-                 &rosen_derivative,
+                 rosen,
+                 rosen_derivative,
                  starting_point,
                  -1);
         cout << "rosen solution: \n"<< starting_point << endl;