Sparse Matrix

Sparse matrices, matrices with a significant proportion of zero entries, are central to efficient computation in numerous fields, aiming to reduce storage and computational costs while maintaining accuracy. Current research focuses on developing and optimizing algorithms for sparse matrix operations (like multiplication and decomposition), exploring novel sparse matrix formats (e.g., value-compressed sparse column), and integrating sparse representations into machine learning models (e.g., sparse neural networks and efficient fine-tuning methods). These advancements have significant implications for accelerating computations in areas such as deep learning, scientific computing, and large-scale data analysis, enabling the handling of increasingly large and complex datasets.