Source Code for Module minimise.steepest_descent

 1  from Numeric import copy, dot 
 2   
 3  from generic_line_search import generic_line_search 
 4  from generic_minimise import generic_minimise 
 5   
 6   
 7 -class steepest_descent(generic_line_search, generic_minimise): 
 8 -        def __init__(self, func, dfunc=None, args=(), x0=None, line_search_algor=None, func_tol=1e-5, maxiter=1000, full_output=0, print_flag=0, a0=1.0, mu=0.0001, eta=0.1): 
 9                  "Class for steepest descent minimisation specific functions." 
10   
11                  self.func = func 
12                  self.dfunc = dfunc 
13                  self.args = args 
14                  self.xk = x0 
15                  self.func_tol = func_tol 
16                  self.maxiter = maxiter 
17                  self.full_output = full_output 
18                  self.print_flag = print_flag 
19   
20                  if not line_search_algor: 
21                          raise NameError, "No line search algorithm has been supplied." 
22                  else: 
23                          self.line_search_algor = line_search_algor 
24   
25                  # Set a0. 
26                  self.a0 = a0 
27   
28                  # Line search constants for the Wolfe conditions. 
29                  self.mu = mu 
30                  self.eta = eta 
31   
32                  # Initialise the function, gradient, and hessian evaluation counters. 
33                  self.f_count = 0 
34                  self.g_count = 0 
35                  self.h_count = 0 
36   
37                  # Initialise the warning string. 
38                  self.warning = None 
39   
40                  # The initial function value and gradient vector. 
41                  self.fk, self.f_count = apply(self.func, (self.xk,)+self.args), self.f_count + 1 
42                  self.dfk, self.g_count = apply(self.dfunc, (self.xk,)+self.args), self.g_count + 1 
43   
44                  # Minimisation. 
45                  self.minimise = self.generic_minimise 
46   
47   
48 -        def backup_current_data(self): 
49                  "Function to backup the current data dfk into dfk_last." 
50   
51                  self.fk_last = self.fk 
52                  self.dfk_last = copy.deepcopy(self.dfk) 
53   
54   
55 -        def dir(self): 
56                  "Return the steepest descent direction." 
57   
58                  self.pk = -self.dfk 
59   
60   
61 -        def get_a0(self): 
62                  "Update a0 using information about the last iteration." 
63   
64                  self.a0 = self.alpha * dot(self.dfk_last, -self.dfk_last) / dot(self.dfk, -self.dfk) 
65   
66   
67 -        def update_data(self): 
68                  "Function to update the function value, gradient vector, and hessian matrix" 
69   
70                  self.xk = copy.deepcopy(self.xk_new) 
71                  self.fk, self.f_count = apply(self.func, (self.xk,)+self.args), self.f_count + 1 
72                  self.dfk, self.g_count = apply(self.dfunc, (self.xk,)+self.args), self.g_count + 1 
73