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#include "ConjugateMinimizer.hpp"
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bool ConjugateMinimizerBase::isSolvable(){
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//conjuage gradient can only solve unconstrained nonlinear model
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if (!model->hasConstraint())
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return true;
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else
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return false;
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}
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void ConjugateMinimizerBase::Init(){
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}
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void ConjugateMinimizerBase::printMinizerInfo(){
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}
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void ConjugateMinimizerBase::Minimize(){
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int maxIteration;
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int nextRestIter;
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int resetFrq;
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int nextWriteIter;
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int writeFrq;
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if (!isSolvable()){
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cout << "ConjugateMinimizerBase Error: This nonlinear model can not be solved by " << methodName <<endl;
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exit(1);
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}
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printMinizerInfo();
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resetFrq = paramSet->getResetFrq();
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nextRestIter = resetFrq;
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writeFrq = paramSet->getWriteFrq();
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nextWriteIter = writeFrq;
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prevGrad = model->calcGrad();
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direction = preGrad;
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maxIteration = paramSet->getMaxIteration();
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for(currentIter = 0;currentIter < maxIteration; currentIter++){
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// perform line search to minimize f(x + stepSize * direction) where stepSize > 0
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lsMinimizer->minimize(direction, 0.0, 1.0);
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lsStatus = lsMinimizer->getMinimizationStatus();
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if(lsStatus ==MINSTATUS_ERROR){
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minStatus = MINSTATUS_ERROR;
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return;
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}
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prevMinX = minX;
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minX = minX + lsMinimizer->getMinVar() * direction;
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//calculate the gradient
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prevGrad = gradient;
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gradient = model->calcGrad();
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// stop if converge
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convStatus = checkConvergence();
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if (convStatus == ){
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writeOut(minX, currentIter);
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minStatus = MINSTATUS_CONVERGE;
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return;
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}
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//calculate the
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gamma = calcGamma(grad, preGrad);
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// update new direction
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prevDirection = direction;
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direction += gamma * direction;
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//
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if (currentIter == nextWriteIter){
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nextWriteIter += writeFrq;
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writeOut(minX, currentIter);
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}
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if (currentIter == nextResetIter){
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reset();
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nextResetIter += resetFrq;
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}
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}
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// if writeFrq is not a multipiler of maxIteration, we need to write the final result
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// otherwise, we already write it inside the loop, just skip it
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if(currentIter != (nextWriteIter - writeFrq))
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writeOut(minX, currentIter);
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minStatus = MINSTATUS_MAXITER;
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return;
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}
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int ConjugateMinimizerBase::checkConvergence(){
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//test absolute gradient tolerance
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if (norm2(gradient) < paramSet->absGradTol)
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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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void ConjugateMinimizerBase::reset(){
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}
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double FRCGMinimizer::calcGamma(vector<double>& newGrad, vector<double>& oldGrad){
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return norm2(newGrad) / norm2(oldGrad);
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}
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double PRCGMinimizer::calcGamma(vector<double>& newGrad, vector<double>& oldGrad){
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double gamma;
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vector<double> deltaGrad;
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deltaGrad = newGrad - oldGrad;
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return norm(deltaGrad, newGrad) / norm2(oldGrad);
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}
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