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 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);

    //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);

  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(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, 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(const 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:	1010
Committed:	Tue Feb 3 20:43:08 2004 UTC (20 years, 5 months ago) by tim
File size:	5294 byte(s)
Log Message:	to avoid cyclic depency, refactory constraint class
#	Content
1	#ifndef _NLMODEL_H_
2	#define _NLMODEL_H_
3
4	#include <vector>
5	#include <utility>
6	#include <math.h>
7
8	#include "SymMatrix.hpp"
9	#include "Functor.hpp"
10	#include "ConstraintList.hpp"
11
12	using namespace std;
13
14
15	typedef enum {backward, forward, central} FDType;
16
17	// special property of nonlinear object function
18	typedef enum {linear, quadratic, general} NLOFProp;
19
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
26	virtual void setX(const vector<double>& x)= 0;
27
28	virtual int getDim() {return ndim;}
29
30	bool hasConstraints() { return constraints == NULL ? false : true;}
31	int getConsType() { return constraints->getConsType();}
32
33	virtual double calcF() = 0;
34	virtual double calcF(const vector<double>& x) = 0;
35	virtual vector<double> calcGrad() = 0;
36	virtual vector<double> calcGrad(vector<double>& x) = 0;
37	virtual SymMatrix calcHessian() = 0;
38	virtual SymMatrix calcHessian(vector<double>& x) = 0;
39
40	#ifdef IS_MPI
41	void setMPIINITFunctor(MPIINITFunctor* func);
42	int getLocalDim() {return localDim;}
43
44	virtual void update(); //a hook function to load balancing
45	#endif
46
47	protected:
48	ConstraintList* constraints; //constraints of nonlinear optimization model
49	int numOfFunEval; //number of function evaluation
50	int ndim;
51
52	#ifdef IS_MPI
53	bool mpiInitFlag;
54	int myRank; //rank of current node
55	int numOfProc; // number of processors
56	MPIINITFunctor * mpiInitFunc;
57
58	int localDim;
59	vector<int> procMappingArray;
60	int beginGlobalIndex;
61	#endif
62	};
63
64	//abstract class of nonlinear optimization model without derivatives
65	class NLModel0 : public NLModel{
66	public:
67
68	NLModel0(int dim, ConstraintList* cons = NULL);
69	~NLModel0() {}
70
71	virtual void setX(const vector<double>& x);
72
73	//Using finite difference methods to approximate the gradient
74	//It is inappropriate to apply these methods in large scale problem
75
76	vector<double> BackwardGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h);
77	vector<double> ForwardGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h);
78	vector<double> CentralGrad(const vector<double>& x, double& fx, vector<double>& grad, const vector<double>& h);
79
80	//Using finite difference methods to approximate the hessian
81	//It is inappropriate to apply this method in large scale problem
82	//virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
83
84	protected:
85
86	FDType fdType;
87	vector<double> currentX;
88	double currentF;
89	};
90
91	//concrete class of nonlinear optimization model without derivatives
92
93	class ConcreteNLMode0 : public NLModel0{
94
95	public:
96
97	ConcreteNLMode0(int dim, ObjFunctor0* func , ConstraintList* cons = NULL);
98	ConcreteNLMode0(int dim, ConstraintList* cons = NULL);
99
100	virtual double calcF();
101	virtual double calcF(const vector<double>& x);
102	virtual vector<double> calcGrad();
103	virtual vector<double> calcGrad(vector<double>& x);
104	virtual SymMatrix calcHessian() ;
105	virtual SymMatrix calcHessian(vector<double>& x) ;
106
107	protected:
108
109	ObjFunctor0* objfunc;
110
111	};
112
113	//abstract class of nonlinear optimization model with first derivatives
114	class NLModel1 : public NLModel0{
115
116	public:
117
118	//Using finite difference methods to approximate the hessian
119	//It is inappropriate to apply this method in large scale problem
120	virtual SymMatrix FiniteHessian(vector<double>& x, vector<double>& h);
121
122	protected:
123
124	vector<double> currentGrad;
125	};
126
127	//concrete class of nonlinear optimization model with first derivatives
128	class ConcreteNLMode1 : NLModel1{
129
130	public:
131
132	ConcreteNLMode1(int dim, ObjFunctor1* func , ConstraintList* cons = NULL);
133	ConcreteNLMode1(int dim, ConstraintList* cons = NULL);
134
135	virtual double calcF();
136	virtual double calcF(const vector<double>& x);
137	virtual vector<double> calcGrad();
138	virtual vector<double> calcGrad(const vector<double>& x);
139	virtual SymMatrix calcHessian() ;
140	virtual SymMatrix calcHessian(vector<double>& x) ;
141
142	protected:
143
144	ObjFunctor1* objfunc;
145	};
146
147	/*
148	//abstract class of nonlinear optimization model with second derivatives
149	class NLModel2 : public NLModel1{
150	public:
151
152	protected:
153	SymMatrix currentHessian;
154
155	};
156
157	//concrete class of nonlinear optimization model with second derivatives
158	class ConcreteNLModel2 : public NLModel2{
159	public:
160
161	ConcreteNLModel2(int dim, ObjFunctor2* func , ConstraintList* cons = NULL);
162	ConcreteNLModel2(int dim, ConstraintList* cons = NULL);
163
164	virtual double calcF();
165	virtual double calcF(const vector<double>& x);
166	virtual vector<double> calcGrad();
167	virtual vector<double> calcGrad(vector<double>& x);
168	virtual SymMatrix calcHessian() ;
169	virtual SymMatrix calcHessian(vector<double>& x) ;
170
171	protected:
172
173	ObjFunctor2* objFunc;
174	};
175	*/
176	#endif