OOPSE/libmdtools/NLModel.hpp

#ifndef _NLMODEL_H_
#define _NLMODEL_H_

#include <vector>
#include <utility>

#include "SymMatrix.hpp"
#include "Functor.hpp"

using namespace std;


typedef enum FDType {backward, forward, central} ;

// special property of nonlinear object function
typedef enum NLOFProp{linear, quadratic, general};

//abstract class of nonlinear optimization model
class NLModel{
  public:    
    NLModel(ConstraintList* cons) {constraints = cons;}
    virtual ~NLModel() {  if (constraints != NULL) delete constraints;}

    virtual void setX(const vector<double>& x)= 0;

    virtual int  getDim() const = 0;

    bool hasConstraints() {  return constraints == NULL ? false : true;}
    int getConsType() {  return constrains->getConsType();}

    virtual double calcF()  = 0;
    virtual double calcF(const vector<double>& x)  = 0;
    virtual vector<double> calcGrad()  = 0;
    virtual vector<double> calcGrad(vector<double>& x)  = 0;
    virtual SymMatrix calcHessian()  = 0;
    virtual SymMatrix calcHessian(vector<double>& x)  = 0;

#ifdef IS_MPI
    void setMPIINITFunctor(MPIINITFunctor* func);
    int getLocalDim() {return localDim;}
    
    virtual void update();  //a hook function to load balancing
#endif

  protected:
    ConstraintList* constraints;       //constraints of nonlinear optimization model
    int numOfFunEval;                  //number of function evaluation

#ifdef IS_MPI
    bool mpiInitFlag;
    int myRank;                           //rank of current node
    int numOfProc;                      // number of processors
    MPIINITFunctor * mpiInitFunc;
    
    int localDim;
    vector<int> procMappingArray; 
    int beginGlobalIndex;   
#endif
};

//abstract class of nonlinear optimization model without derivatives
class NLModel0 : public NLModel{
  public:

    NLModel0(int dim,  ConstraintList* cons = NULL);
    ~NLModel0() {}

    virtual void setX(const vector<double>& x);

    //Using finite difference methods to approximate the gradient
    //It is inappropriate to apply these methods in large scale problem
    
    vector<double> BackwardGrad(const vector<double>& x, double& fx, vector<double>& grad);
    vector<double> ForwardGrad(const vector<double>& x, double& fx, vector<double>& grad);
    vector<double> CentralGrad(const vector<double>& x, double& fx, vector<double>& grad);

    //Using finite difference methods to approximate the hessian
    //It is inappropriate to apply this method in large scale problem
    virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);

  protected:

    FDType fdType;
    vector<double> currentX;
    double curretF;
};

//concrete class of nonlinear optimization model without derivatives

class ConcreteNLMode0 : public NLModel0{

  public:

    ConcreteNLMode0(int dim, ObjFunctor0* func ,  ConstraintList* cons = NULL);
    ConcreteNLMode0(int dim, ConstraintList* cons = NULL);

    virtual double calcF();
    virtual double calcF(const vector<double>& x);
    virtual vector<double> calcGrad();
    virtual vector<double> calcGrad(vector<double>& x);
    virtual SymMatrix calcHessian() ;
    virtual SymMatrix calcHessian(vector<double>& x) ; 
    
  protected:

    ObjFunctor0* objfunc;

};

//abstract class of nonlinear optimization model with first derivatives
class NLModel1 : public NLModel0{

  public:
    
    //Using finite difference methods to approximate the hessian
    //It is inappropriate to apply this method in large scale problem    
    virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
    
  protected:

    vector<double> currentGrad;
};

//concrete class of nonlinear optimization model with first derivatives
class ConcreteNLMode1 : NLModel1{

  public:

    ConcreteNLMode1(int dim, ObjFunctor1* func ,  ConstraintList* cons = NULL);
    ConcreteNLMode1(int dim, ConstraintList* cons = NULL);
    
    virtual double calcF();
    virtual double calcF(const vector<double>& x);
    virtual vector<double> calcGrad();
    virtual vector<double> calcGrad(vector<double>& x);
    virtual SymMatrix calcHessian() ;
    virtual SymMatrix calcHessian(vector<double>& x) ; 

  protected:

    ObjFunctor1* objfunc;
};

/*
//abstract class of nonlinear optimization model with second derivatives
class NLModel2 : public NLModel1{
  public:
    
  protected:    
    SymMatrix currentHessian;

};

//concrete class of nonlinear optimization model with second derivatives
class ConcreteNLModel2 : public NLModel2{
  public:

    ConcreteNLModel2(int dim, ObjFunctor2* func ,  ConstraintList* cons = NULL);
    ConcreteNLModel2(int dim, ConstraintList* cons = NULL);
    
    virtual double calcF();
    virtual double calcF(const vector<double>& x);
    virtual vector<double> calcGrad();
    virtual vector<double> calcGrad(vector<double>& x);
    virtual SymMatrix calcHessian() ;
    virtual SymMatrix calcHessian(vector<double>& x) ;
    
  protected:
    
    ObjFunctor2* objFunc;
};
*/
#endif
Revision:	1000
Committed:	Fri Jan 30 21:47:22 2004 UTC (21 years, 9 months ago) by tim
File size:	5150 byte(s)
Log Message:	using class Minimizer1D derived from MinimizerBase instead of a functor to do line seach
#	User	Rev	Content
1	tim	995	#ifndef _NLMODEL_H_
2			#define _NLMODEL_H_
3
4			#include <vector>
5	tim	996	#include <utility>
6	tim	995
7			#include "SymMatrix.hpp"
8			#include "Functor.hpp"
9
10			using namespace std;
11
12	tim	996
13	tim	995	typedef enum FDType {backward, forward, central} ;
14
15	tim	996	// special property of nonlinear object function
16			typedef enum NLOFProp{linear, quadratic, general};
17	tim	995
18			//abstract class of nonlinear optimization model
19			class NLModel{
20			public:
21			NLModel(ConstraintList* cons) {constraints = cons;}
22			virtual ~NLModel() { if (constraints != NULL) delete constraints;}
23	tim	1000
24	tim	995	virtual void setX(const vector<double>& x)= 0;
25
26			virtual int getDim() const = 0;
27
28			bool hasConstraints() { return constraints == NULL ? false : true;}
29			int getConsType() { return constrains->getConsType();}
30
31			virtual double calcF() = 0;
32			virtual double calcF(const vector<double>& x) = 0;
33			virtual vector<double> calcGrad() = 0;
34			virtual vector<double> calcGrad(vector<double>& x) = 0;
35			virtual SymMatrix calcHessian() = 0;
36			virtual SymMatrix calcHessian(vector<double>& x) = 0;
37
38			#ifdef IS_MPI
39			void setMPIINITFunctor(MPIINITFunctor* func);
40	tim	996	int getLocalDim() {return localDim;}
41
42			virtual void update(); //a hook function to load balancing
43	tim	995	#endif
44
45			protected:
46			ConstraintList* constraints; //constraints of nonlinear optimization model
47			int numOfFunEval; //number of function evaluation
48
49			#ifdef IS_MPI
50			bool mpiInitFlag;
51	tim	996	int myRank; //rank of current node
52			int numOfProc; // number of processors
53	tim	995	MPIINITFunctor * mpiInitFunc;
54	tim	996
55	tim	995	int localDim;
56	tim	996	vector<int> procMappingArray;
57			int beginGlobalIndex;
58	tim	995	#endif
59			};
60
61			//abstract class of nonlinear optimization model without derivatives
62			class NLModel0 : public NLModel{
63			public:
64
65			NLModel0(int dim, ConstraintList* cons = NULL);
66			~NLModel0() {}
67
68	tim	1000	virtual void setX(const vector<double>& x);
69	tim	995
70			//Using finite difference methods to approximate the gradient
71			//It is inappropriate to apply these methods in large scale problem
72
73			vector<double> BackwardGrad(const vector<double>& x, double& fx, vector<double>& grad);
74			vector<double> ForwardGrad(const vector<double>& x, double& fx, vector<double>& grad);
75			vector<double> CentralGrad(const vector<double>& x, double& fx, vector<double>& grad);
76
77			//Using finite difference methods to approximate the hessian
78			//It is inappropriate to apply this method in large scale problem
79	tim	1000	virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
80	tim	995
81	tim	1000	protected:
82
83	tim	995	FDType fdType;
84			vector<double> currentX;
85	tim	1000	double curretF;
86	tim	995	};
87
88	tim	1000	//concrete class of nonlinear optimization model without derivatives
89
90			class ConcreteNLMode0 : public NLModel0{
91
92			public:
93
94			ConcreteNLMode0(int dim, ObjFunctor0* func , ConstraintList* cons = NULL);
95			ConcreteNLMode0(int dim, ConstraintList* cons = NULL);
96
97			virtual double calcF();
98			virtual double calcF(const vector<double>& x);
99			virtual vector<double> calcGrad();
100			virtual vector<double> calcGrad(vector<double>& x);
101			virtual SymMatrix calcHessian() ;
102			virtual SymMatrix calcHessian(vector<double>& x) ;
103
104			protected:
105
106			ObjFunctor0* objfunc;
107
108			};
109
110	tim	995	//abstract class of nonlinear optimization model with first derivatives
111			class NLModel1 : public NLModel0{
112	tim	1000
113	tim	995	public:
114
115			//Using finite difference methods to approximate the hessian
116			//It is inappropriate to apply this method in large scale problem
117	tim	1000	virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
118	tim	995
119			protected:
120	tim	1000
121	tim	995	vector<double> currentGrad;
122			};
123
124	tim	996	//concrete class of nonlinear optimization model with first derivatives
125			class ConcreteNLMode1 : NLModel1{
126	tim	1000
127	tim	995	public:
128	tim	1000
129	tim	996	ConcreteNLMode1(int dim, ObjFunctor1* func , ConstraintList* cons = NULL);
130			ConcreteNLMode1(int dim, ConstraintList* cons = NULL);
131	tim	995
132			virtual double calcF();
133			virtual double calcF(const vector<double>& x);
134			virtual vector<double> calcGrad();
135			virtual vector<double> calcGrad(vector<double>& x);
136			virtual SymMatrix calcHessian() ;
137			virtual SymMatrix calcHessian(vector<double>& x) ;
138
139			protected:
140	tim	1000
141	tim	995	ObjFunctor1* objfunc;
142			};
143
144			/*
145	tim	1000	//abstract class of nonlinear optimization model with second derivatives
146	tim	995	class NLModel2 : public NLModel1{
147			public:
148	tim	1000
149			protected:
150			SymMatrix currentHessian;
151	tim	995
152	tim	1000	};
153
154			//concrete class of nonlinear optimization model with second derivatives
155			class ConcreteNLModel2 : public NLModel2{
156			public:
157
158			ConcreteNLModel2(int dim, ObjFunctor2* func , ConstraintList* cons = NULL);
159			ConcreteNLModel2(int dim, ConstraintList* cons = NULL);
160	tim	995
161			virtual double calcF();
162			virtual double calcF(const vector<double>& x);
163			virtual vector<double> calcGrad();
164			virtual vector<double> calcGrad(vector<double>& x);
165			virtual SymMatrix calcHessian() ;
166			virtual SymMatrix calcHessian(vector<double>& x) ;
167
168			protected:
169
170			ObjFunctor2* objFunc;
171			};
172			*/
173			#endif