OOPSE/libmdtools/Minimizer1D.cpp

#include "Minimizer1D.hpp"
#include "math.h"

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;

      prevMinVar = minVar;
      fPrevMinVar = fMinVar;
      
      minVar = beta;
      fMinVar = model->calcF(tempX);
      
    }
    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;

      prevMinVar = minVar;
      fPrevMinVar = fMinVar;
      
      minVar = alpha;
      fMinVar = model->calcF(tempX);
    }
     
  }

  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;
  
  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();   

     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;

     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:	1015
Committed:	Tue Feb 3 22:54:52 2004 UTC (20 years, 5 months ago) by tim
File size:	5233 byte(s)
Log Message:	NLModel0, NLModel1 pass uit test
#	User	Rev	Content
1	tim	1002	#include "Minimizer1D.hpp"
2	tim	1005	#include "math.h"
3
4			GoldenSectionMinimizer::GoldenSectionMinimizer(NLModel* nlp)
5			:Minimizer1D(nlp){
6			setName("GoldenSection");
7			}
8	tim	1002
9	tim	1005	int GoldenSectionMinimizer::checkConvergence(){
10
11	tim	1002	if ((rightVar - leftVar) < stepTol)
12	tim	1010	return 1;
13	tim	1002	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	tim	1015	tempX = currentX = model->getX();
23	tim	1002
24			alpha = leftVar + (1 - goldenRatio) * (rightVar - leftVar);
25			beta = leftVar + goldenRatio * (rightVar - leftVar);
26
27	tim	1015	for (int i = 0; i < tempX.size(); i ++)
28			tempX[i] = currentX[i] + direction[i] * alpha;
29
30	tim	1002	fAlpha = model->calcF(tempX);
31
32	tim	1015	for (int i = 0; i < tempX.size(); i ++)
33			tempX[i] = currentX[i] + direction[i] * beta;
34
35	tim	1002	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			beta = leftVar + goldenRatio * (rightVar - leftVar);
48
49	tim	1015	for (int i = 0; i < tempX.size(); i ++)
50			tempX[i] = currentX[i] + direction[i] * beta;
51	tim	1002
52			prevMinVar = minVar;
53			fPrevMinVar = fMinVar;
54
55			minVar = beta;
56			fMinVar = model->calcF(tempX);
57
58			}
59			else{
60			rightVar = beta;
61			beta = alpha;
62			alpha = leftVar + (1 - goldenRatio) * (rightVar - leftVar);
63
64	tim	1015	for (int i = 0; i < tempX.size(); i ++)
65			tempX[i] = currentX[i] + direction[i] * alpha;
66	tim	1002
67			prevMinVar = minVar;
68			fPrevMinVar = fMinVar;
69
70			minVar = alpha;
71			fMinVar = model->calcF(tempX);
72			}
73
74			}
75
76			minStatus = MINSTATUS_MAXITER;
77
78			}
79
80	tim	1005	/**
81			* Brent's method is a root-finding algorithm which combines root bracketing, interval bisection,
82			* and inverse quadratic interpolation.
83	tim	1002	*/
84	tim	1005	BrentMinimizer::BrentMinimizer(NLModel* nlp)
85			:Minimizer1D(nlp){
86			setName("Brent");
87			}
88	tim	1002
89			void BrentMinimizer::minimize(){
90
91	tim	1005	double fu, fv, fw;
92			double p, q, r;
93			double u, v, w;
94			double d;
95			double e;
96			double etemp;
97			double stepTol2;
98	tim	1010	double fLeftVar, fRightVar;
99	tim	1005	const double goldenRatio = 0.3819660;
100			vector<double> tempX, currentX;
101
102			stepTol2 = 2 * stepTol;
103			e = 0;
104			d = 0;
105
106			currentX = tempX = model->getX();
107
108	tim	1015	for (int i = 0; i < tempX.size(); i ++)
109			tempX[i] = currentX[i] + direction[i] * leftVar;
110
111	tim	1010	fLeftVar = model->calcF(tempX);
112	tim	1015
113			for (int i = 0; i < tempX.size(); i ++)
114			tempX[i] = currentX[i] + direction[i] * rightVar;
115	tim	1005
116	tim	1010	fRightVar = model->calcF(tempX);
117	tim	1005
118	tim	1010	if(fRightVar < fLeftVar) {
119			prevMinVar = rightVar;
120			fPrevMinVar = fRightVar;
121	tim	1005	v = leftVar;
122			fv = fLeftVar;
123			}
124			else {
125			prevMinVar = leftVar;
126	tim	1010	fPrevMinVar = fLeftVar;
127	tim	1005	v = rightVar;
128	tim	1010	fv = fRightVar;
129	tim	1005	}
130	tim	1010
131			midVar = leftVar + rightVar;
132	tim	1005
133	tim	1002	for(currentIter = 0; currentIter < maxIteration; currentIter){
134
135	tim	1005	// a trial parabolic fit
136			if (fabs(e) > stepTol){
137	tim	1002
138	tim	1005	r = (minVar - prevMinVar) * (fMinVar - fv);
139			q = (minVar - v) * (fMinVar - fPrevMinVar);
140			p = (minVar - v) q -(minVar - prevMinVar)r;
141			q = 2.0 *(q-r);
142	tim	1002
143	tim	1005	if (q > 0.0)
144			p = -p;
145	tim	1002
146	tim	1005	q = fabs(q);
147
148			etemp = e;
149			e = d;
150
151	tim	1010	if(fabs(p) >= fabs(0.5qetemp) \|\| p <= q(leftVar - minVar) \|\| p >= q(rightVar - minVar)){
152	tim	1005	e = minVar >= midVar ? leftVar - minVar : rightVar - minVar;
153			d = goldenRatio * e;
154			}
155			else{
156			d = p/q;
157			u = minVar + d;
158			if ( u - leftVar < stepTol2 \|\| rightVar - u < stepTol2)
159			d = midVar > minVar ? stepTol : - stepTol;
160			}
161			}
162			//golden section
163			else{
164			e = minVar >=midVar? leftVar - minVar : rightVar - minVar;
165			d =goldenRatio * e;
166			}
167
168			u = fabs(d) >= stepTol ? minVar + d : minVar + copysign(d, stepTol);
169
170	tim	1015	for (int i = 0; i < tempX.size(); i ++)
171			tempX[i] = currentX[i] + direction[i] * u;
172
173	tim	1005	fu = model->calcF();
174
175			if(fu <= fMinVar){
176
177			if(u >= minVar)
178			leftVar = minVar;
179			else
180			rightVar = minVar;
181
182			v = prevMinVar;
183			fv = fPrevMinVar;
184			prevMinVar = minVar;
185			fPrevMinVar = fMinVar;
186			minVar = u;
187			fMinVar = fu;
188
189			}
190			else{
191			if (u < minVar) leftVar = u;
192			else rightVar= u;
193			if(fu <= fPrevMinVar \|\| prevMinVar == minVar) {
194			v = prevMinVar;
195			fv = fPrevMinVar;
196			prevMinVar = u;
197			fPrevMinVar = fu;
198			}
199			else if ( fu <= fv \|\| v == minVar \|\| v == prevMinVar ) {
200			v = u;
201			fv = fu;
202			}
203			}
204
205			midVar = leftVar + rightVar;
206
207			if (checkConvergence() > 0){
208			minStatus = MINSTATUS_CONVERGE;
209			return;
210			}
211
212	tim	1002	}
213
214
215			minStatus = MINSTATUS_MAXITER;
216	tim	1010	return;
217	tim	1002	}
218	tim	1005
219	tim	1010	int BrentMinimizer::checkConvergence(){
220	tim	1005
221			if (fabs(minVar - midVar) < stepTol)
222			return 1;
223			else
224			return -1;
225			}