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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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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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currentX = tempX = model->getX(); |
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tempX = currentX + leftVar * direction; |
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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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fLeftVar = 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] * rightVar; |
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tempX = currentX + rightVar * direction; |
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fRightVar = model->calcF(tempX); |
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if(fRightVar < fLeftVar) { |
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fv = fRightVar; |
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} |
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midVar = leftVar + rightVar; |
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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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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; |