Sparse Matrix Techniques

Sparse matrix techniques involve specialized methods for efficiently storing and processing matrices that contain mostly zeros. Instead of storing every element, these techniques focus only on the non-zero values and their locations, significantly reducing memory usage and computational effort. They are commonly used in fields like machine learning, network analysis, and scientific computing, where large matrices with many zeros are common. By exploiting the sparsity, these methods enable faster calculations and better scalability, making it feasible to handle complex problems that would be impractical with traditional dense matrix approaches.