👉 Sparse Matrix (SVM) is a data structure used to efficiently store and manipulate matrices that contain a large number of zero elements. Unlike dense matrices, where every element is stored, SVMs only store non-zero elements along with their row and column indices, significantly reducing memory usage. This makes SVMs particularly useful in applications like linear algebra, machine learning, and scientific computing, where matrices often have many zeros. SVMs typically use specialized algorithms to perform operations like matrix multiplication and inversion, optimizing these processes for sparse data.
