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#ifndef _NLMODEL_H_
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#define _NLMODEL_H_
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#include <vector>
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#include "SymMatrix.hpp"
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#include "Functor.hpp"
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using namespace std;
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typedef enum FDType {backward, forward, central} ;
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typedef enum {linear, quadratic, general};
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//abstract class of nonlinear optimization model
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class NLModel{
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public:
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NLModel(ConstraintList* cons) {constraints = cons;}
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virtual ~NLModel() { if (constraints != NULL) delete constraints;}
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virtual void setX(const vector<double>& x)= 0;
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virtual void setF(const vector<double>& fx)= 0;
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virtual int getDim() const = 0;
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bool hasConstraints() { return constraints == NULL ? false : true;}
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int getConsType() { return constrains->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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virtual vector<double> calcGrad() = 0;
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virtual vector<double> calcGrad(vector<double>& x) = 0;
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virtual SymMatrix calcHessian() = 0;
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virtual SymMatrix calcHessian(vector<double>& x) = 0;
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#ifdef IS_MPI
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void setMPIINITFunctor(MPIINITFunctor* func);
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#endif
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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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#ifdef IS_MPI
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bool mpiInitFlag;
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MPIINITFunctor * mpiInitFunc;
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int localDim;
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#endif
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};
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//abstract class of nonlinear optimization model without derivatives
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class NLModel0 : public NLModel{
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public:
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NLModel0(int dim, ConstraintList* cons = NULL);
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~NLModel0() {}
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protected:
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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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//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 FDHessian(vector<double>& sx);
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FDType fdType;
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vector<double> currentX;
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};
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//abstract class of nonlinear optimization model with first derivatives
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class NLModel1 : public NLModel0{
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public:
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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 FDHessian(vector<double>& sx);
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protected:
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vector<double> currentGrad;
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};
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class NLF1 : NLModel1{
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public:
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NLModel1(int dim, ObjFunctor1* func , ConstraintList* cons = NULL);
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NLModel1(int dim, ConstraintList* cons = NULL);
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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 SymMatrix calcHessian() ;
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virtual SymMatrix calcHessian(vector<double>& x) ;
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protected:
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ObjFunctor1* objfunc;
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};
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/*
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class NLModel2 : public NLModel1{
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public:
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NLModel2(int dim, ObjFunctor2* func , ConstraintList* cons = NULL);
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~NLModel2() {}
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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 SymMatrix calcHessian() ;
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virtual SymMatrix calcHessian(vector<double>& x) ;
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protected:
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SymMatrix hessian;
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ObjFunctor2* objFunc;
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};
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*/
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#endif
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