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#include "SteepestDescent.hpp"
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#include "Utility.hpp"
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void SteepestDescent::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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direction = model->calcGrad();
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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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direction = model->calcGrad(minX);
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// stop if converge
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if (checkConvergence() > 0){
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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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//
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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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} |
