OOPSE/libmdtools/NLModel.hpp

#ifndef _NLMODEL_H_
#define _NLMODEL_H_

#include <vector>
#include <utility>
#include <math.h>

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

using namespace std;


typedef enum  {backward, forward, central} FDType;

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

//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 vector<double> getX() = 0;

    virtual void setF(double f) = 0;
    virtual double getF() = 0;

    virtual int  getDim() {return ndim;}

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

#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) {currentX = x;}
    vector<double>  getX() {return currentX;}

    void setF(double f) {currentF = f;}
    double getF() {return currentF;}

    //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, const vector<double>& h);
    vector<double> ForwardGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h);
    vector<double> CentralGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h);

    //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);
    SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
  protected:

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

//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(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, vector<double>& h);

    void setGrad(vector<double>& grad) {currentGrad = grad;}
    vector<double> getGrad() {return currentGrad;}
  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(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(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:	1015
Committed:	Tue Feb 3 22:54:52 2004 UTC (20 years, 5 months ago) by tim
File size:	5713 byte(s)
Log Message:	NLModel0, NLModel1 pass uit test
#	User	Rev	Content
1	tim	995	#ifndef _NLMODEL_H_
2			#define _NLMODEL_H_
3
4			#include <vector>
5	tim	996	#include <utility>
6	tim	1010	#include <math.h>
7	tim	995
8			#include "SymMatrix.hpp"
9			#include "Functor.hpp"
10	tim	1010	#include "ConstraintList.hpp"
11	tim	995
12			using namespace std;
13
14	tim	996
15	tim	1010	typedef enum {backward, forward, central} FDType;
16	tim	995
17	tim	996	// special property of nonlinear object function
18	tim	1010	typedef enum {linear, quadratic, general} NLOFProp;
19	tim	995
20			//abstract class of nonlinear optimization model
21			class NLModel{
22			public:
23			NLModel(ConstraintList* cons) {constraints = cons;}
24			virtual ~NLModel() { if (constraints != NULL) delete constraints;}
25	tim	1000
26	tim	995	virtual void setX(const vector<double>& x)= 0;
27	tim	1015	virtual vector<double> getX() = 0;
28	tim	995
29	tim	1015	virtual void setF(double f) = 0;
30			virtual double getF() = 0;
31
32	tim	1010	virtual int getDim() {return ndim;}
33	tim	995
34			bool hasConstraints() { return constraints == NULL ? false : true;}
35	tim	1010	int getConsType() { return constraints->getConsType();}
36	tim	995
37			virtual double calcF() = 0;
38			virtual double calcF(const vector<double>& x) = 0;
39			virtual vector<double> calcGrad() = 0;
40			virtual vector<double> calcGrad(vector<double>& x) = 0;
41			virtual SymMatrix calcHessian() = 0;
42			virtual SymMatrix calcHessian(vector<double>& x) = 0;
43
44			#ifdef IS_MPI
45			void setMPIINITFunctor(MPIINITFunctor* func);
46	tim	996	int getLocalDim() {return localDim;}
47
48			virtual void update(); //a hook function to load balancing
49	tim	995	#endif
50
51			protected:
52			ConstraintList* constraints; //constraints of nonlinear optimization model
53			int numOfFunEval; //number of function evaluation
54	tim	1010	int ndim;
55	tim	995
56			#ifdef IS_MPI
57			bool mpiInitFlag;
58	tim	996	int myRank; //rank of current node
59			int numOfProc; // number of processors
60	tim	995	MPIINITFunctor * mpiInitFunc;
61	tim	996
62	tim	995	int localDim;
63	tim	996	vector<int> procMappingArray;
64			int beginGlobalIndex;
65	tim	995	#endif
66			};
67
68			//abstract class of nonlinear optimization model without derivatives
69			class NLModel0 : public NLModel{
70			public:
71
72			NLModel0(int dim, ConstraintList* cons = NULL);
73			~NLModel0() {}
74
75	tim	1015	virtual void setX(const vector<double>& x) {currentX = x;}
76			vector<double> getX() {return currentX;}
77	tim	995
78	tim	1015	void setF(double f) {currentF = f;}
79			double getF() {return currentF;}
80
81	tim	995	//Using finite difference methods to approximate the gradient
82			//It is inappropriate to apply these methods in large scale problem
83
84	tim	1010	vector<double> BackwardGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h);
85			vector<double> ForwardGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h);
86			vector<double> CentralGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h);
87	tim	995
88			//Using finite difference methods to approximate the hessian
89			//It is inappropriate to apply this method in large scale problem
90	tim	1010	//virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
91	tim	1015	SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
92	tim	1000	protected:
93
94	tim	995	FDType fdType;
95			vector<double> currentX;
96	tim	1010	double currentF;
97	tim	995	};
98
99	tim	1000	//concrete class of nonlinear optimization model without derivatives
100
101			class ConcreteNLMode0 : public NLModel0{
102
103			public:
104
105			ConcreteNLMode0(int dim, ObjFunctor0* func , ConstraintList* cons = NULL);
106			ConcreteNLMode0(int dim, ConstraintList* cons = NULL);
107
108			virtual double calcF();
109	tim	1015	virtual double calcF(vector<double>& x);
110	tim	1000	virtual vector<double> calcGrad();
111			virtual vector<double> calcGrad(vector<double>& x);
112			virtual SymMatrix calcHessian() ;
113			virtual SymMatrix calcHessian(vector<double>& x) ;
114
115			protected:
116
117			ObjFunctor0* objfunc;
118
119			};
120
121	tim	995	//abstract class of nonlinear optimization model with first derivatives
122			class NLModel1 : public NLModel0{
123	tim	1000
124	tim	995	public:
125
126			//Using finite difference methods to approximate the hessian
127			//It is inappropriate to apply this method in large scale problem
128	tim	1010	virtual SymMatrix FiniteHessian(vector<double>& x, vector<double>& h);
129	tim	1015
130			void setGrad(vector<double>& grad) {currentGrad = grad;}
131			vector<double> getGrad() {return currentGrad;}
132	tim	995	protected:
133	tim	1000
134	tim	995	vector<double> currentGrad;
135			};
136
137	tim	996	//concrete class of nonlinear optimization model with first derivatives
138			class ConcreteNLMode1 : NLModel1{
139	tim	1000
140	tim	995	public:
141	tim	1000
142	tim	996	ConcreteNLMode1(int dim, ObjFunctor1* func , ConstraintList* cons = NULL);
143			ConcreteNLMode1(int dim, ConstraintList* cons = NULL);
144	tim	995
145			virtual double calcF();
146	tim	1015	virtual double calcF(vector<double>& x);
147	tim	995	virtual vector<double> calcGrad();
148	tim	1015	virtual vector<double> calcGrad( vector<double>& x);
149	tim	995	virtual SymMatrix calcHessian() ;
150			virtual SymMatrix calcHessian(vector<double>& x) ;
151
152			protected:
153	tim	1000
154	tim	995	ObjFunctor1* objfunc;
155			};
156
157			/*
158	tim	1000	//abstract class of nonlinear optimization model with second derivatives
159	tim	995	class NLModel2 : public NLModel1{
160			public:
161	tim	1000
162			protected:
163			SymMatrix currentHessian;
164	tim	995
165	tim	1000	};
166
167			//concrete class of nonlinear optimization model with second derivatives
168			class ConcreteNLModel2 : public NLModel2{
169			public:
170
171			ConcreteNLModel2(int dim, ObjFunctor2* func , ConstraintList* cons = NULL);
172			ConcreteNLModel2(int dim, ConstraintList* cons = NULL);
173	tim	995
174			virtual double calcF();
175	tim	1015	virtual double calcF(vector<double>& x);
176	tim	995	virtual vector<double> calcGrad();
177			virtual vector<double> calcGrad(vector<double>& x);
178			virtual SymMatrix calcHessian() ;
179			virtual SymMatrix calcHessian(vector<double>& x) ;
180
181			protected:
182
183			ObjFunctor2* objFunc;
184			};
185			*/
186			#endif