Sparse Matrices
A sparse matrix is a matrix that has a large number of zero elements compared to the total number of elements in the matrix. In other words, it is a matrix where most of the entries are zero.

Explanation
Sparse matrices are commonly encountered in various fields such as scientific computing, data analysis, and graph theory. They arise when dealing with large datasets or networks where only a small fraction of the possible connections actually exist.

Many Zero Entries
The correct answer is option 'A' - sparse matrices have many zero entries. This means that the majority of the elements in a sparse matrix are zero. The zero entries represent the absence of a value or the lack of a connection in the dataset or network.

Advantages of Sparse Matrices
The sparsity of a matrix can provide several advantages in terms of computational efficiency and memory usage:

1. Reduced Storage Space: Storing only the non-zero elements of a sparse matrix can significantly reduce the memory requirements compared to storing all the elements. This is especially important when dealing with large datasets or networks.

2. Faster Computations: Performing computations on sparse matrices can be much faster compared to dense matrices. Since most of the entries are zero, operations involving these zero entries can be skipped, resulting in faster computations.

3. Efficient Algorithms: Several specialized algorithms have been developed specifically for sparse matrices, taking advantage of their sparsity to achieve faster and more efficient computations. These algorithms can be used for tasks such as matrix multiplication, solving linear systems, and calculating eigenvalues and eigenvectors.

4. Improved Interpretability: In certain applications, such as data analysis and graph theory, the sparsity of a matrix can provide meaningful insights. The zero entries can represent missing connections or relationships, allowing for a more interpretable representation of the data.

Conclusion
Sparse matrices have many zero entries, making them different from dense matrices where most entries are non-zero. The sparsity of a matrix provides various advantages in terms of storage space, computational efficiency, and interpretability. Understanding and working with sparse matrices is essential in many fields of computer science and data analysis.