Matrix Compression

Matrix compression aims to reduce the storage and computational demands of large matrices, crucial for handling massive datasets in various fields. Current research focuses on developing efficient algorithms, such as hierarchical matrices, quantization techniques (including low-rank and low-precision factorizations), and novel compression schemes tailored for specific applications like large language models (LLMs) and key-value caching. These advancements are significantly impacting fields like machine learning, computer vision, and scientific computing by enabling the processing of previously intractable datasets and accelerating inference speeds for resource-intensive applications. The development of near-lossless compression methods is a key area of ongoing investigation.