Source Code for Module minimise.constraint_linear

 1  ############################################################################### 
 2  #                                                                             # 
 3  # Copyright (C) 2003 Edward d'Auvergne                                        # 
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21  ############################################################################### 
22   
23   
24  from Numeric import dot, matrixmultiply 
25   
26   
27 -class Constraint_linear: 
28 -    def __init__(self, A=None, b=None): 
29          """Class for the creation of linear inequality constraint functions and gradients. 
30   
31          The constraints are in the form: 
32   
33              A.x >= b 
34   
35          where: 
36              A is an m*n matrix where the rows are the transposed vectors, ai, of length 
37              n.  The elements of ai are the coefficients of the model parameters. 
38   
39              x is the vector of model parameters of dimension n. 
40   
41              b is the vector of scalars of dimension m. 
42   
43              m is the number of constraints. 
44   
45              n is the number of model parameters. 
46   
47   
48          eg if 0 <= q <= 1, q >= 1 - 2r, and 0 <= r, then: 
49   
50              | 1  0 |            |  0 | 
51              |      |            |    | 
52              |-1  0 |   | q |    | -1 | 
53              |      | . |   | >= |    | 
54              | 1  2 |   | r |    |  1 | 
55              |      |            |    | 
56              | 0  1 |            |  2 | 
57          """ 
58   
59          # Initialise arguments. 
60          self.A = A 
61          self.b = b 
62   
63   
64 -    def func(self, x): 
65          """The constraint function. 
66   
67          A vector containing the constraint values is returned. 
68          """ 
69   
70          return dot(self.A, x) - self.b 
71   
72   
73 -    def dfunc(self, x): 
74          """The constraint gradient. 
75   
76          As the inequality constraints are linear, the gradient matrix is constant and equal to the 
77          coefficient matrix A.  Therefore this function simply returns the matrix. 
78          """ 
79   
80          return self.A 
81