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#include "Minimizer1D.hpp" |
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#include "math.h" |
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//----------------------------------------------------------------------------// |
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void Minimizer1D::Minimize(vector<double>& direction), double left, double right); { |
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setDirection(direction); |
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setRange(left,right); |
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minimize(); |
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} |
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//----------------------------------------------------------------------------// |
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#include "Utility.hpp" |
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GoldenSectionMinimizer::GoldenSectionMinimizer(NLModel* nlp) |
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:Minimizer1D(nlp){ |
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setName("GoldenSection"); |
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int GoldenSectionMinimizer::checkConvergence(){ |
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if ((rightVar - leftVar) < stepTol) |
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return 1 |
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return 1; |
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else |
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return -1; |
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} |
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const double goldenRatio = 0.618034; |
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currentX = model->getX(); |
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tempX = currentX = model->getX(); |
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alpha = leftVar + (1 - goldenRatio) * (rightVar - leftVar); |
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beta = leftVar + goldenRatio * (rightVar - leftVar); |
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tempX = currentX + direction * alpha; |
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for (int i = 0; i < tempX.size(); i ++) |
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tempX[i] = currentX[i] + direction[i] * alpha; |
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fAlpha = model->calcF(tempX); |
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tempX = currentX + direction * beta; |
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for (int i = 0; i < tempX.size(); i ++) |
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tempX[i] = currentX[i] + direction[i] * beta; |
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fBeta = model->calcF(tempX); |
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for(currentIter = 0; currentIter < maxIteration; currentIter++){ |
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if (fAlpha > fBeta){ |
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leftVar = alpha; |
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alpha = beta; |
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beta = leftVar + goldenRatio * (rightVar - leftVar); |
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tempX = currentX + beta * direction; |
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prevMinVar = minVar; |
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fPrevMinVar = fMinVar; |
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for (int i = 0; i < tempX.size(); i ++) |
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tempX[i] = currentX[i] + direction[i] * beta; |
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fAlpha = fBeta; |
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fBeta = model->calcF(tempX); |
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prevMinVar = alpha; |
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fPrevMinVar = fAlpha; |
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minVar = beta; |
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fMinVar = model->calcF(tempX); |
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fMinVar = fBeta; |
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} |
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else{ |
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rightVar = beta; |
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beta = alpha; |
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alpha = leftVar + (1 - goldenRatio) * (rightVar - leftVar); |
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tempX = currentX + alpha * direction; |
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for (int i = 0; i < tempX.size(); i ++) |
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tempX[i] = currentX[i] + direction[i] * alpha; |
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prevMinVar = minVar; |
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fPrevMinVar = fMinVar; |
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fBeta = fAlpha; |
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fAlpha = model->calcF(tempX); |
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prevMinVar = beta; |
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fPrevMinVar = fBeta; |
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minVar = alpha; |
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fMinVar = model->calcF(tempX); |
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fMinVar = fAlpha; |
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} |
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} |
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double e; |
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double etemp; |
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double stepTol2; |
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double fLeft, fRight; |
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double fLeftVar, fRightVar; |
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const double goldenRatio = 0.3819660; |
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vector<double> tempX, currentX; |
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currentX = tempX = model->getX(); |
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tempX = currentX + leftVar * direction; |
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fLeft = model->calcF(tempX); |
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for (int i = 0; i < tempX.size(); i ++) |
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tempX[i] = currentX[i] + direction[i] * leftVar; |
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tempX = currentX + rightVar * direction; |
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fRight = model->calcF(tempX); |
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fLeftVar = model->calcF(tempX); |
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if(fRight < fLeft) { |
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prevMinPoint = rightVar; |
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fPrevMinVar = fRight; |
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for (int i = 0; i < tempX.size(); i ++) |
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tempX[i] = currentX[i] + direction[i] * rightVar; |
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fRightVar = model->calcF(tempX); |
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if(fRightVar < fLeftVar) { |
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prevMinVar = rightVar; |
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fPrevMinVar = fRightVar; |
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v = leftVar; |
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fv = fLeftVar; |
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} |
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else { |
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prevMinVar = leftVar; |
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fPrevMinVar = fLeft; |
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fPrevMinVar = fLeftVar; |
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v = rightVar; |
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fv = fRight; |
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fv = fRightVar; |
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} |
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midVar = (leftVar + rightVar) / 2; |
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for(currentIter = 0; currentIter < maxIteration; currentIter){ |
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for(currentIter = 0; currentIter < maxIteration; currentIter++){ |
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// a trial parabolic fit |
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if (fabs(e) > stepTol){ |
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etemp = e; |
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e = d; |
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if(fabs(p) >= fabs(0.5*q*etemp)) || p <= q*(leftVar - minVar) || p >= q*(rightVar - minVar)){ |
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if(fabs(p) >= fabs(0.5*q*etemp) || p <= q*(leftVar - minVar) || p >= q*(rightVar - minVar)){ |
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e = minVar >= midVar ? leftVar - minVar : rightVar - minVar; |
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d = goldenRatio * e; |
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} |
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u = fabs(d) >= stepTol ? minVar + d : minVar + copysign(d, stepTol); |
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tempX = currentX + u * direction; |
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fu = model->calcF(); |
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for (int i = 0; i < tempX.size(); i ++) |
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tempX[i] = currentX[i] + direction[i] * u; |
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fu = model->calcF(tempX); |
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if(fu <= fMinVar){ |
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} |
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} |
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midVar = leftVar + rightVar; |
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midVar = (leftVar + rightVar) /2; |
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if (checkConvergence() > 0){ |
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minStatus = MINSTATUS_CONVERGE; |
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minStatus = MINSTATUS_MAXITER; |
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return; |
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//----------------------------------------------------------------------------// |
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return; |
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} |
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BrentMinimizer::checkConvergence(){ |
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int BrentMinimizer::checkConvergence(){ |
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if (fabs(minVar - midVar) < stepTol) |
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return 1; |