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 (20 years, 5 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
#	Content
1	#ifndef _NLMODEL_H_
2	#define _NLMODEL_H_
3
4	#include <vector>
5	#include <utility>
6
7	#include "SymMatrix.hpp"
8	#include "Functor.hpp"
9
10	using namespace std;
11
12
13	typedef enum FDType {backward, forward, central} ;
14
15	// special property of nonlinear object function
16	typedef enum NLOFProp{linear, quadratic, general};
17
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
24	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	int getLocalDim() {return localDim;}
41
42	virtual void update(); //a hook function to load balancing
43	#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	int myRank; //rank of current node
52	int numOfProc; // number of processors
53	MPIINITFunctor * mpiInitFunc;
54
55	int localDim;
56	vector<int> procMappingArray;
57	int beginGlobalIndex;
58	#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	virtual void setX(const vector<double>& x);
69
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	virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
80
81	protected:
82
83	FDType fdType;
84	vector<double> currentX;
85	double curretF;
86	};
87
88	//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	//abstract class of nonlinear optimization model with first derivatives
111	class NLModel1 : public NLModel0{
112
113	public:
114
115	//Using finite difference methods to approximate the hessian
116	//It is inappropriate to apply this method in large scale problem
117	virtual SymMatrix FiniteHessian(vector<double>& x, double fx, vector<double>& h);
118
119	protected:
120
121	vector<double> currentGrad;
122	};
123
124	//concrete class of nonlinear optimization model with first derivatives
125	class ConcreteNLMode1 : NLModel1{
126
127	public:
128
129	ConcreteNLMode1(int dim, ObjFunctor1* func , ConstraintList* cons = NULL);
130	ConcreteNLMode1(int dim, ConstraintList* cons = NULL);
131
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
141	ObjFunctor1* objfunc;
142	};
143
144	/*
145	//abstract class of nonlinear optimization model with second derivatives
146	class NLModel2 : public NLModel1{
147	public:
148
149	protected:
150	SymMatrix currentHessian;
151
152	};
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
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