What are the benefits and limitations of using sparse matrices in numerical linear algebra?
Sparse matrices are matrices that have mostly zero entries, and they often arise in numerical linear algebra when dealing with large-scale problems. For example, you might encounter sparse matrices when solving partial differential equations, graph theory, network analysis, or data compression. In this article, you will learn what are the benefits and limitations of using sparse matrices in numerical linear algebra, and how to handle them efficiently and accurately.