Compressed Sparse Row
Compressed Sparse Row (CSR) is a data structure and storage format used to efficiently represent sparse matrices, where most elements are zero. It stores only the non-zero values along with their column indices and row pointers, significantly reducing memory usage compared to dense representations. This format is widely used in scientific computing, graph algorithms, and machine learning for operations on large sparse datasets.
Developers should learn CSR when working with sparse matrices in applications like linear algebra solvers, network analysis, or natural language processing, where memory efficiency is critical. It enables faster matrix-vector multiplication and other operations by avoiding computations on zero elements, making it essential for high-performance computing and large-scale data processing.