Changed example to use minimization rather than maximization.

python_examples/global_optimization.py

@@ -3,8 +3,8 @@
 #
 # 
 # This is an example illustrating the use of the global optimization routine,
-# find_max_global(), from the dlib C++ Library.  This is a tool for finding the
+# find_min_global(), from the dlib C++ Library.  This is a tool for finding the
-# inputs to a function that result in the function giving its maximal output.
+# inputs to a function that result in the function giving its minimal output.
 # This is a very useful tool for hyper parameter search when applying machine
 # learning methods.  There are also many other applications for this kind of
 # general derivative free optimization.  However, in this example program, we
@@ -30,17 +30,17 @@ import dlib
 from math import sin,cos,pi,exp,sqrt
 
 # This is a standard test function for these kinds of optimization problems.
-# It has a bunch of local maxima, with the global maximum resulting in
+# It has a bunch of local minima, with the global minimum resulting in
-# holder_table()==19.2085025679. 
+# holder_table()==-19.2085025679. 
 def holder_table(x0,x1):
-    return abs(sin(x0)*cos(x1)*exp(abs(1-sqrt(x0*x0+x1*x1)/pi)))
+    return -abs(sin(x0)*cos(x1)*exp(abs(1-sqrt(x0*x0+x1*x1)/pi)))
 
 # Find the optimal inputs to holder_table().  The print statements that follow
-# show that find_max_global() finds the optimal settings to high precision.
+# show that find_min_global() finds the optimal settings to high precision.
-x,y = dlib.find_max_global(holder_table, 
+x,y = dlib.find_min_global(holder_table, 
                            [-10,-10],  # Lower bound constraints on x0 and x1 respectively
                            [10,10],    # Upper bound constraints on x0 and x1 respectively
-                           80)         # The number of times find_max_global() will call holder_table()
+                           80)         # The number of times find_min_global() will call holder_table()
 
 print("optimal inputs: {}".format(x));
 print("optimal output: {}".format(y));