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#include <vector> |
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#include <utility> |
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#include <math.h> |
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#include "SymMatrix.hpp" |
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#include "Functor.hpp" |
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#include "ConstraintList.hpp" |
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using namespace std; |
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typedef enum FDType {backward, forward, central} ; |
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typedef enum {backward, forward, central} FDType; |
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// special property of nonlinear object function |
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typedef enum NLOFProp{linear, quadratic, general}; |
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typedef enum {linear, quadratic, general} NLOFProp; |
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//abstract class of nonlinear optimization model |
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class NLModel{ |
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virtual void setX(const vector<double>& x)= 0; |
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virtual int getDim() const = 0; |
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virtual int getDim() {return ndim;} |
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bool hasConstraints() { return constraints == NULL ? false : true;} |
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int getConsType() { return constrains->getConsType();} |
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int getConsType() { return constraints->getConsType();} |
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virtual double calcF() = 0; |
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virtual double calcF(const vector<double>& x) = 0; |
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protected: |
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ConstraintList* constraints; //constraints of nonlinear optimization model |
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int numOfFunEval; //number of function evaluation |
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int ndim; |
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#ifdef IS_MPI |
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bool mpiInitFlag; |
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//Using finite difference methods to approximate the gradient |
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//It is inappropriate to apply these methods in large scale problem |
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vector<double> BackwardGrad(const vector<double>& x, double& fx, vector<double>& grad); |
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vector<double> ForwardGrad(const vector<double>& x, double& fx, vector<double>& grad); |
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vector<double> CentralGrad(const vector<double>& x, double& fx, vector<double>& grad); |
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vector<double> BackwardGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h); |
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vector<double> ForwardGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h); |
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vector<double> CentralGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h); |
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//Using finite difference methods to approximate the hessian |
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//It is inappropriate to apply this method in large scale problem |
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virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h); |
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//virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h); |
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protected: |
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FDType fdType; |
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vector<double> currentX; |
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double curretF; |
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double currentF; |
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}; |
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//concrete class of nonlinear optimization model without derivatives |
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//Using finite difference methods to approximate the hessian |
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//It is inappropriate to apply this method in large scale problem |
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virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h); |
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virtual SymMatrix FiniteHessian(vector<double>& x, vector<double>& h); |
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protected: |
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virtual double calcF(); |
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virtual double calcF(const vector<double>& x); |
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virtual vector<double> calcGrad(); |
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virtual vector<double> calcGrad(vector<double>& x); |
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virtual vector<double> calcGrad(const vector<double>& x); |
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virtual SymMatrix calcHessian() ; |
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virtual SymMatrix calcHessian(vector<double>& x) ; |
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