A sparse matrix is a type of matrix in which most of the elements are zero. This characteristic makes it efficient to store and process, as it allows for the use of specialized data structures that only keep track of the non-zero elements. Sparse matrices are commonly used in various fields, including computer science, engineering, and applied mathematics, where large datasets often contain many zero values. In contrast to dense matrices, which store every element regardless of its value, sparse matrices can significantly reduce memory usage and computational time. They are particularly useful in applications like machine learning, graph theory, and scientific computing, where large-scale problems often involve sparse data representations.