operation use standard dense-matrix structures and algorithms are slow and inefficient when apply to large sparse matrix as processing and memory are wasted on the zeros. When storing and manipulate sparse matrix on a computer, it is beneficial and often necessary to use specialized algorithms and data structures that take advantage of the sparse structure of the matrix. The term sparse matrix was possibly coined by Harry Markowitz who triggered some pioneering work but then leave the field.

COMING SOON!

```
/*A sparse matrix is a matrix which has number of zeroes greater than (m*n)/2,
where m and n are the dimensions of the matrix.*/
#include <iostream>
using namespace std;
int main()
{
int m, n;
int counterZeros = 0;
cout << "Enter dimensions of matrix (seperated with space): ";
cin >> m >> n;
int a[m][n];
cout << "Enter matrix elements:";
cout << "\n";
// reads the matrix from stdin
for (int i = 0; i < m; i++)
{
for (int j = 0; j < n; j++)
{
cout << "element? ";
cin >> a[i][j];
}
}
// counts the zero's
for (int i = 0; i < m; i++)
{
for (int j = 0; j < n; j++)
{
if (a[i][j] == 0)
counterZeros++; //Counting number of zeroes
}
}
// makes sure the matrix is a sparse matrix
if (counterZeros > ((m * n) / 2)) //Checking for sparse matrix
cout << "Sparse matrix";
else
cout << "Not a sparse matrix";
}
```