blob: 5ae554c2156c1bb58f0723c4e4fcbc72a3a79507 (
plain)
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
56
57
58
59
60
|
package optimization.stopCriteria;
import optimization.gradientBasedMethods.Objective;
import optimization.gradientBasedMethods.ProjectedObjective;
import optimization.util.MathUtils;
/**
* Divides the norm by the norm at the begining of the iteration
* @author javg
*
*/
public class NormalizedProjectedGradientL2Norm extends ProjectedGradientL2Norm{
/**
* Stop if gradientNorm/(originalGradientNorm) smaller
* than gradientConvergenceValue
*/
double originalProjectedNorm = -1;
public NormalizedProjectedGradientL2Norm(double gradientConvergenceValue){
super(gradientConvergenceValue);
}
public void reset(){
originalProjectedNorm = -1;
}
double[] projectGradient(ProjectedObjective obj){
if(obj.auxParameters == null){
obj.auxParameters = new double[obj.getNumParameters()];
}
System.arraycopy(obj.getParameters(), 0, obj.auxParameters, 0, obj.getNumParameters());
MathUtils.minusEquals(obj.auxParameters, obj.gradient, 1);
obj.auxParameters = obj.projectPoint(obj.auxParameters);
MathUtils.minusEquals(obj.auxParameters,obj.getParameters(),1);
return obj.auxParameters;
}
public boolean stopOptimization(Objective obj){
if(obj instanceof ProjectedObjective) {
ProjectedObjective o = (ProjectedObjective) obj;
double norm = MathUtils.L2Norm(projectGradient(o));
if(originalProjectedNorm == -1){
originalProjectedNorm = norm;
}
double normalizedNorm = 1.0*norm/originalProjectedNorm;
if( normalizedNorm < gradientConvergenceValue){
System.out.println("Gradient norm below normalized normtreshold: " + norm + " original: " + originalProjectedNorm + " normalized norm: " + normalizedNorm);
return true;
}else{
// System.out.println("projected gradient norm: " + norm);
return false;
}
}
System.out.println("Not a projected objective");
throw new RuntimeException();
}
}
|
