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#ifndef TNT_SPARSE_MATRIX_H
#define TNT_SPARSE_MATRIX_H
/*
*
* Template Numerical Toolkit (TNT)
*
* Mathematical and Computational Sciences Division
* National Institute of Technology,
* Gaithersburg, MD USA
*
*
* This software was developed at the National Institute of Standards and
* Technology (NIST) by employees of the Federal Government in the course
* of their official duties. Pursuant to title 17 Section 105 of the
* United States Code, this software is not subject to copyright protection
* and is in the public domain. NIST assumes no responsibility whatsoever for
* its use by other parties, and makes no guarantees, expressed or implied,
* about its quality, reliability, or any other characteristic.
*
*/
#include <vector>
#include "tnt_vector.h"
#include "tnt_sparse_vector.h"
namespace TNT
{
//namespace Linear_Algebra
//{
#if 0
template <class T, class Integer>
class Sparse_Matrix_Coordinate_Element
{
private:
T val_;
Integer row_index_;
Integer col_index_;
public:
Sparse_Matrix_Coordinate_Element(
const T& a, const Integer &i, const Integer &j) :
val_(a), row_index_(i), col_index(i) {}
const T& value() const { return val_; }
Integer row_index() const { return row_index_; }
Integer col_index() const { return col_index_; }
T& value() { return val_; }
Integer& row_index() { return row_index_; }
Integer& col_index() { return col_index_; }
};
#endif
/**
Read-only view of a sparse matrix in compressed-row storage
format. Neither array elements (nonzeros) nor sparsity
structure can be modified. If modifications are required,
create a new view.
<p>
Index values begin at 0.
<p>
<b>Storage requirements:</b> An (m x n) matrix with
nz nonzeros requires no more than ((T+2I)*nz)
bytes, where T is the size of data elements and
I is the size of integer subscripts.
*/
template <class T>
class Sparse_Matrix
{
private:
// compressed row storage M rows of <value, col_index> pairs
//
std::vector< Sparse_Vector<T> > S_;
int num_rows_; // number of rows
int num_cols_; // number of cols
int num_nonzeros_; // number of nonzeros
// Used only in multi-step constructions. This
// allows one to build a sparse matrix with
// multiple calls to insert().
//
int internal_state_; // 0 if closed (no more inserts) , 1 if open;
public:
Sparse_Matrix(Subscript M, Subscript N):
S_(M),
num_rows_(M), num_cols_(N), num_nonzeros_(0),
internal_state_(1) {};
Sparse_Matrix(Subscript M, Subscript N, Subscript nz, const T* val,
const Subscript *r, const Subscript *c):
S_(M),
num_rows_(M),
num_cols_(N),
num_nonzeros_(0),
internal_state_(1)
{
insert(nz, val, r, c);
close();
};
int is_closed() { return internal_state_; }
void insert(const T& val, Subscript i, Subscript j)
{
if (internal_state_ == 0) return;
S_[i].insert(val, j);
num_nonzeros_++;
}
void insert(Subscript nz, const T* val, const Subscript *i,
const Subscript *j)
{
if (internal_state_ == 0) return;
for (int count=0; count<nz; count++)
{
insert(val[count], i[count], j[count]);
}
}
void insert_base_one(const T& val, Subscript i, Subscript j)
{
insert_one_base(val, i-1, j-1);
}
void insert_base_one(Subscript nz, const T* val, const Subscript *i,
const Subscript *j)
{
for (int count=0; count<nz; count++)
{
insert(val[count], i[count]-1, j[count]-1);
}
}
void close()
{
/*
After this, there are no more inserts. Now one could optimize
storage layout.
*/
internal_state_ = 0;
}
inline int num_rows() const {return num_rows_;}
inline int num_cols() const {return num_cols_;}
inline int num_columns() const {return num_cols_;}
int num_nonzeros() const {return num_nonzeros_;}
Vector<T> diag() const
{
int minMN = num_rows() < num_columns() ? num_rows() : num_columns();
Vector<T> diag_(minMN, T(0));
for (int i=0; i<minMN; i++)
{
for (typename Sparse_Vector<T>::const_iterator p = S_[i].begin();
p < S_[i].end(); p++ )
{
if (p->index() == i)
{
diag_[i] += p->value();
break;
}
}
}
return diag_;
}
Vector<T> mult(const Vector<T> &x) const
{
int M = num_rows();
Vector<T> y(M);
for (int i=0; i<M; i++)
{
y[i] = dot_product(S_[i], x);
}
return y;
}
inline double norm() const
{
T sum(0.0);
for (int i=0; i<num_rows_; i++)
{
for (typename Sparse_Vector<T>::const_iterator p = S_[i].begin();
p < S_[i].end(); p++ )
{
sum += p->value() * p->value();
}
}
return sqrt(sum);
}
std::ostream & print(std::ostream &s) const
{
for (int i=0; i<num_rows_; i++)
{
for (typename Sparse_Vector<T>::const_iterator p = S_[i].begin();
p < S_[i].end(); p++ )
{
s << "( " << p->value() << " , " << i << ", " << p->index() << " )\n";
}
}
return s;
}
std::ostream & print_base_one(std::ostream &s) const
{
for (int i=0; i<num_rows_; i++)
{
for (typename Sparse_Vector<T>::const_iterator p = S_[i].begin();
p < S_[i].end(); p++ )
{
s <<"( "<<p->value()<<" , "<<i+1<<", "<< p->index()+1 << " )\n";
}
}
return s;
}
};
template <class T>
inline Vector<T> operator*(const Sparse_Matrix<T> &S, const Vector<T> &x)
{
return S.mult(x);
}
template <class T>
inline double norm(const Sparse_Matrix<T> &S)
{
return S.norm();
}
template <class T>
inline std::ostream& operator<<(std::ostream &s, const Sparse_Matrix<T> &A)
{
return A.print(s);
}
//} /* namspace TNT::Linear_Algebra */
} /* namspace TNT */
#endif
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