What Is Sparse Matrix in Data Structure?


Larry Thompson

A sparse matrix is a data structure used to represent a matrix in which most of the elements are zero. This type of matrix is particularly useful in situations where the matrix is large and contains mostly zero values, as it can save both memory and computational resources.

Why Use Sparse Matrix?

Traditional methods of representing matrices store all the elements, including the zeros, in a two-dimensional array. However, when dealing with large matrices that are sparse, this can be extremely inefficient. Storing all the zeros takes up unnecessary memory space and requires additional computational operations.

By using a sparse matrix representation, we can store only the non-zero elements along with their respective row and column indices. This allows us to reduce memory usage and optimize various operations on the matrix.

Sparse Matrix Representation

There are several methods to represent a sparse matrix:

1. Coordinate List (COO)

The coordinate list method represents each non-zero element by storing its row index, column index, and value in separate lists or arrays. This representation is simple but not suitable for performing efficient arithmetic operations on the matrix.

2. Compressed Sparse Row (CSR)

The compressed sparse row method stores the non-zero elements in three separate arrays: one for values, one for column indices, and one for row pointers.

The row pointer array indicates where each row starts in the values and column indices arrays. This representation enables fast access to rows but slower access to individual columns.

3. Compressed Sparse Column (CSC)

The compressed sparse column method is similar to CSR but stores data by columns instead of rows.

It uses three arrays: one for values, one for row indices, and one for column pointers. This representation enables fast access to columns but slower access to individual rows.

Operations on Sparse Matrix

Using a sparse matrix representation allows us to perform various operations more efficiently:

1. Addition and Subtraction

When adding or subtracting two matrices, we only need to perform operations on the non-zero elements. This can significantly reduce the computational complexity and improve performance. Multiplication

The multiplication of sparse matrices can also be performed more efficiently by skipping the multiplication of elements that are guaranteed to be zero.

3. Transposition

The transposition of a sparse matrix can be done by simply swapping the row and column indices of each non-zero element, without affecting the zero elements.


Sparse matrices are a valuable tool when working with large matrices that have a significant number of zeros. By using an appropriate sparse matrix representation, we can save memory space, reduce computational complexity, and optimize various matrix operations. Understanding and utilizing sparse matrices can greatly improve performance in applications involving large datasets and numerical computations.

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