-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathdeadzone.py
More file actions
55 lines (42 loc) · 1.51 KB
/
deadzone.py
File metadata and controls
55 lines (42 loc) · 1.51 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
"""
Copyright 2013 Steven Diamond
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import division
import cvxopt
from cvxpy import Minimize, Problem, Variable, maximum
# Taken from CVX website http://cvxr.com/cvx/examples/
# Section 6.1.2: Residual minimization with deadzone penalty
# Ported from cvx matlab to cvxpy by Misrab Faizullah-Khan
# Original comments below
# Boyd & Vandenberghe "Convex Optimization"
# Joelle Skaf - 08/17/05
#
# The penalty function approximation problem has the form:
# minimize sum(deadzone(Ax - b))
# where 'deadzone' is the deadzone penalty function
# deadzone(y) = max(abs(y)-1,0)
# Input data
m = 16
n = 8
A = cvxopt.normal(m,n)
b = cvxopt.normal(m,1)
# Formulate the problem
x = Variable(n)
objective = Minimize( sum(maximum( abs(A*x -b) - 1 , 0 )) )
p = Problem(objective, [])
# Solve it
print ('Computing the optimal solution of the deadzone approximation problem:')
p.solve()
print ('Optimal vector:')
print (x.value)
print ('Residual vector:')
print (A*x.value - b)