👉 Sparse Matrix Computation (SPARCS) is a method designed to efficiently handle large matrices that are mostly filled with zeros, known as sparse matrices. Unlike dense matrix operations, which require significant memory and computational resources for non-zero elements, SPARCS focuses only on the non-zero elements, storing them in a compact format (often using a compressed row or column storage). This approach drastically reduces memory usage and speeds up operations like matrix multiplication, factorization, and inversion by avoiding unnecessary computations on zero elements. SPARCS is particularly useful in fields like scientific computing, machine learning, and data analysis where large datasets with many zeros are common.
