Sparse Matrix Compression Using Relaxed Pruning and Huffman Coding

The patent divides the matrix processing into multiple levels using a hierarchical structure. Large sparse matrices are segmented into smaller sub-matrices or blocks, which are then processed independently. This segmentation allows the CSC format to maintain effective compression rates by applying compression algorithms to smaller, more manageable units rather than attempting to compress the entire large matrix at once, thus resolving the contradiction between processing efficiency and compression rate.

The patent transforms the representation parameters of sparse matrix elements by encoding them in a hierarchical format that preserves essential information while reducing memory footprint. Instead of traditional binary or fixed-precision representations, the invention uses variable-length encoding schemes that adapt to the sparsity pattern, maintaining processing speed by preserving critical numerical relationships while significantly reducing memory usage through optimized parameter encoding.

The patent introduces a hierarchical dimension to matrix representation, organizing sparse matrix data in a multi-level structure that adds a dimensional layer to traditional flat matrix storage. This hierarchical dimensionality allows large matrices to be decomposed into nested levels of sub-matrices, enabling efficient compression and selective processing that reduces transmission and processing costs while maintaining the accuracy benefits of larger matrix sizes through intelligent data organization and retrieval.