OOPSE/libmdtools/Minimizer1D.cpp

#include "Minimizer1D.hpp"
#include "math.h"
#include "Utility.hpp"
GoldenSectionMinimizer::GoldenSectionMinimizer(NLModel* nlp)
                               :Minimizer1D(nlp){
  setName("GoldenSection");
}

int GoldenSectionMinimizer::checkConvergence(){

  if ((rightVar - leftVar) < stepTol)
    return 1;
  else
    return -1;
}
void GoldenSectionMinimizer::minimize(){
  vector<double> tempX;
  vector <double> currentX;

  const double goldenRatio = 0.618034;
  
  tempX = currentX =  model->getX();

  alpha = leftVar + (1 - goldenRatio) * (rightVar  - leftVar);
  beta = leftVar + goldenRatio * (rightVar - leftVar);

  for (int i = 0; i < tempX.size(); i ++)
    tempX[i] = currentX[i] + direction[i] * alpha;

  fAlpha = model->calcF(tempX);

  for (int i = 0; i < tempX.size(); i ++)
    tempX[i] = currentX[i] + direction[i] * beta;

  fBeta = model->calcF(tempX);

  for(currentIter = 0; currentIter < maxIteration; currentIter++){

     if (checkConvergence() > 0){
       minStatus = MINSTATUS_CONVERGE;
       return;
     }
    
    if (fAlpha > fBeta){
      leftVar = alpha;
      alpha = beta;
      
      beta =  leftVar + goldenRatio * (rightVar - leftVar);

      for (int i = 0; i < tempX.size(); i ++)
        tempX[i] = currentX[i] + direction[i] * beta;
      fAlpha = fBeta;
      fBeta  = model->calcF(tempX);
      
      prevMinVar = alpha;
      fPrevMinVar = fAlpha;      
      minVar = beta;
      fMinVar = fBeta;      
    }
    else{
      rightVar = beta;
      beta = alpha;

      alpha = leftVar + (1 - goldenRatio) * (rightVar  - leftVar);

      for (int i = 0; i < tempX.size(); i ++)
        tempX[i] = currentX[i] + direction[i] * alpha;

      fBeta = fAlpha;
      fAlpha = model->calcF(tempX);

      prevMinVar = beta;
      fPrevMinVar = fBeta;

      minVar = alpha;
      fMinVar = fAlpha;       
    }
     
  }

  minStatus = MINSTATUS_MAXITER;
    
}

/**
 * Brent's method is a root-finding algorithm which combines root bracketing, interval bisection,
 * and inverse quadratic interpolation.
 */
BrentMinimizer::BrentMinimizer(NLModel* nlp)
                   :Minimizer1D(nlp){
  setName("Brent");
}

void BrentMinimizer::minimize(){

  double fu, fv, fw;
  double p, q, r;
  double u, v, w;
  double d;
  double e;
  double etemp;
  double stepTol2;
  double fLeftVar, fRightVar;
  const double goldenRatio = 0.3819660;
  vector<double> tempX, currentX;
  
  stepTol2 = 2 * stepTol;
  e = 0;
  d = 0;

  currentX = tempX = model->getX();

  for (int i = 0; i < tempX.size(); i ++)
    tempX[i] = currentX[i] + direction[i] * leftVar;
  
  fLeftVar = model->calcF(tempX);

  for (int i = 0; i < tempX.size(); i ++)
    tempX[i] = currentX[i] + direction[i] * rightVar;  
  
  fRightVar = model->calcF(tempX);

  if(fRightVar < fLeftVar) {
    prevMinVar = rightVar;
    fPrevMinVar = fRightVar;
    v  = leftVar;
    fv = fLeftVar;
  }
  else {
    prevMinVar = leftVar;
    fPrevMinVar = fLeftVar;
    v  = rightVar;
    fv = fRightVar;
  }

  midVar = (leftVar + rightVar) / 2;
  
  for(currentIter = 0; currentIter < maxIteration; currentIter++){

     // a trial parabolic fit
     if (fabs(e) > stepTol){

       r = (minVar - prevMinVar) * (fMinVar - fv);
       q = (minVar - v) * (fMinVar - fPrevMinVar);
       p = (minVar - v) *q -(minVar - prevMinVar)*r;
       q = 2.0 *(q-r);

       if (q > 0.0)
         p = -p;

       q = fabs(q);

       etemp = e;
       e  = d;

       if(fabs(p) >= fabs(0.5*q*etemp) || p <= q*(leftVar - minVar) || p >= q*(rightVar - minVar)){
         e =  minVar >= midVar ? leftVar - minVar : rightVar - minVar;
         d = goldenRatio * e;
       }
       else{
         d = p/q;
         u = minVar + d;
         if ( u - leftVar < stepTol2 || rightVar - u  < stepTol2)
           d = midVar > minVar ? stepTol : - stepTol;
       }
     }
     //golden section
     else{
       e = minVar >=midVar? leftVar - minVar : rightVar - minVar;
       d =goldenRatio * e;
     }

     u = fabs(d) >= stepTol ? minVar + d : minVar + copysign(d, stepTol);

     for (int i = 0; i < tempX.size(); i ++)
       tempX[i] = currentX[i] + direction[i] * u;  
    
     fu = model->calcF(tempX);   

     if(fu <= fMinVar){

       if(u >= minVar)
         leftVar = minVar;
       else
         rightVar = minVar;

       v  = prevMinVar;
       fv = fPrevMinVar;
       prevMinVar = minVar;
       fPrevMinVar = fMinVar;
       minVar = u;
       fMinVar = fu;
       
     }
     else{
       if (u < minVar) leftVar = u;
       else rightVar= u;
       if(fu <= fPrevMinVar || prevMinVar == minVar) {
         v  = prevMinVar;
         fv = fPrevMinVar;
         prevMinVar = u;
         fPrevMinVar = fu;
      }
      else if ( fu <= fv || v == minVar || v == prevMinVar ) {
        v  = u;
         fv = fu;
      }   
    }    

    midVar = (leftVar + rightVar) /2;

     if (checkConvergence() > 0){
       minStatus = MINSTATUS_CONVERGE;
       return;
     }
    
  }


  minStatus = MINSTATUS_MAXITER;
  return;  
}

int BrentMinimizer::checkConvergence(){
  
  if (fabs(minVar - midVar) <  stepTol)
    return 1;
  else
    return -1;
}
Revision:	1023
Committed:	Wed Feb 4 22:26:00 2004 UTC (21 years, 2 months ago) by tim
File size:	5375 byte(s)
Log Message:	Fix a bunch of bugs :-) Single version of conjugate gradient with golden search linesearch pass a couple of functions test. Brent's algorithm is still broken
#	Content
1	#include "Minimizer1D.hpp"
2	#include "math.h"
3	#include "Utility.hpp"
4	GoldenSectionMinimizer::GoldenSectionMinimizer(NLModel* nlp)
5	:Minimizer1D(nlp){
6	setName("GoldenSection");
7	}
8
9	int GoldenSectionMinimizer::checkConvergence(){
10
11	if ((rightVar - leftVar) < stepTol)
12	return 1;
13	else
14	return -1;
15	}
16	void GoldenSectionMinimizer::minimize(){
17	vector<double> tempX;
18	vector <double> currentX;
19
20	const double goldenRatio = 0.618034;
21
22	tempX = currentX = model->getX();
23
24	alpha = leftVar + (1 - goldenRatio) * (rightVar - leftVar);
25	beta = leftVar + goldenRatio * (rightVar - leftVar);
26
27	for (int i = 0; i < tempX.size(); i ++)
28	tempX[i] = currentX[i] + direction[i] * alpha;
29
30	fAlpha = model->calcF(tempX);
31
32	for (int i = 0; i < tempX.size(); i ++)
33	tempX[i] = currentX[i] + direction[i] * beta;
34
35	fBeta = model->calcF(tempX);
36
37	for(currentIter = 0; currentIter < maxIteration; currentIter++){
38
39	if (checkConvergence() > 0){
40	minStatus = MINSTATUS_CONVERGE;
41	return;
42	}
43
44	if (fAlpha > fBeta){
45	leftVar = alpha;
46	alpha = beta;
47
48	beta = leftVar + goldenRatio * (rightVar - leftVar);
49
50	for (int i = 0; i < tempX.size(); i ++)
51	tempX[i] = currentX[i] + direction[i] * beta;
52	fAlpha = fBeta;
53	fBeta = model->calcF(tempX);
54
55	prevMinVar = alpha;
56	fPrevMinVar = fAlpha;
57	minVar = beta;
58	fMinVar = fBeta;
59	}
60	else{
61	rightVar = beta;
62	beta = alpha;
63
64	alpha = leftVar + (1 - goldenRatio) * (rightVar - leftVar);
65
66	for (int i = 0; i < tempX.size(); i ++)
67	tempX[i] = currentX[i] + direction[i] * alpha;
68
69	fBeta = fAlpha;
70	fAlpha = model->calcF(tempX);
71
72	prevMinVar = beta;
73	fPrevMinVar = fBeta;
74
75	minVar = alpha;
76	fMinVar = fAlpha;
77	}
78
79	}
80
81	minStatus = MINSTATUS_MAXITER;
82
83	}
84
85	/**
86	* Brent's method is a root-finding algorithm which combines root bracketing, interval bisection,
87	* and inverse quadratic interpolation.
88	*/
89	BrentMinimizer::BrentMinimizer(NLModel* nlp)
90	:Minimizer1D(nlp){
91	setName("Brent");
92	}
93
94	void BrentMinimizer::minimize(){
95
96	double fu, fv, fw;
97	double p, q, r;
98	double u, v, w;
99	double d;
100	double e;
101	double etemp;
102	double stepTol2;
103	double fLeftVar, fRightVar;
104	const double goldenRatio = 0.3819660;
105	vector<double> tempX, currentX;
106
107	stepTol2 = 2 * stepTol;
108	e = 0;
109	d = 0;
110
111	currentX = tempX = model->getX();
112
113	for (int i = 0; i < tempX.size(); i ++)
114	tempX[i] = currentX[i] + direction[i] * leftVar;
115
116	fLeftVar = model->calcF(tempX);
117
118	for (int i = 0; i < tempX.size(); i ++)
119	tempX[i] = currentX[i] + direction[i] * rightVar;
120
121	fRightVar = model->calcF(tempX);
122
123	if(fRightVar < fLeftVar) {
124	prevMinVar = rightVar;
125	fPrevMinVar = fRightVar;
126	v = leftVar;
127	fv = fLeftVar;
128	}
129	else {
130	prevMinVar = leftVar;
131	fPrevMinVar = fLeftVar;
132	v = rightVar;
133	fv = fRightVar;
134	}
135
136	midVar = (leftVar + rightVar) / 2;
137
138	for(currentIter = 0; currentIter < maxIteration; currentIter++){
139
140	// a trial parabolic fit
141	if (fabs(e) > stepTol){
142
143	r = (minVar - prevMinVar) * (fMinVar - fv);
144	q = (minVar - v) * (fMinVar - fPrevMinVar);
145	p = (minVar - v) q -(minVar - prevMinVar)r;
146	q = 2.0 *(q-r);
147
148	if (q > 0.0)
149	p = -p;
150
151	q = fabs(q);
152
153	etemp = e;
154	e = d;
155
156	if(fabs(p) >= fabs(0.5qetemp) \|\| p <= q(leftVar - minVar) \|\| p >= q(rightVar - minVar)){
157	e = minVar >= midVar ? leftVar - minVar : rightVar - minVar;
158	d = goldenRatio * e;
159	}
160	else{
161	d = p/q;
162	u = minVar + d;
163	if ( u - leftVar < stepTol2 \|\| rightVar - u < stepTol2)
164	d = midVar > minVar ? stepTol : - stepTol;
165	}
166	}
167	//golden section
168	else{
169	e = minVar >=midVar? leftVar - minVar : rightVar - minVar;
170	d =goldenRatio * e;
171	}
172
173	u = fabs(d) >= stepTol ? minVar + d : minVar + copysign(d, stepTol);
174
175	for (int i = 0; i < tempX.size(); i ++)
176	tempX[i] = currentX[i] + direction[i] * u;
177
178	fu = model->calcF(tempX);
179
180	if(fu <= fMinVar){
181
182	if(u >= minVar)
183	leftVar = minVar;
184	else
185	rightVar = minVar;
186
187	v = prevMinVar;
188	fv = fPrevMinVar;
189	prevMinVar = minVar;
190	fPrevMinVar = fMinVar;
191	minVar = u;
192	fMinVar = fu;
193
194	}
195	else{
196	if (u < minVar) leftVar = u;
197	else rightVar= u;
198	if(fu <= fPrevMinVar \|\| prevMinVar == minVar) {
199	v = prevMinVar;
200	fv = fPrevMinVar;
201	prevMinVar = u;
202	fPrevMinVar = fu;
203	}
204	else if ( fu <= fv \|\| v == minVar \|\| v == prevMinVar ) {
205	v = u;
206	fv = fu;
207	}
208	}
209
210	midVar = (leftVar + rightVar) /2;
211
212	if (checkConvergence() > 0){
213	minStatus = MINSTATUS_CONVERGE;
214	return;
215	}
216
217	}
218
219
220	minStatus = MINSTATUS_MAXITER;
221	return;
222	}
223
224	int BrentMinimizer::checkConvergence(){
225
226	if (fabs(minVar - midVar) < stepTol)
227	return 1;
228	else
229	return -1;
230	}