Batch-OMP: An Enhanced Orthogonal Matching Pursuit Algorithm with Batch Processing

Batch-OMP is an enhanced version of the Orthogonal Matching Pursuit algorithm that accelerates computation by dividing the problem into smaller batches. The algorithm employs an iterative approach where data is incrementally added and processed in batches during each iteration, effectively reducing memory consumption. This implementation typically involves processing subsets of data sequentially, with each batch undergoing sparse representation estimation using OMP's core mechanism of selecting the most correlated atoms and updating residuals. Additionally, Batch-OMP leverages parallel computing capabilities to further accelerate processing speed, a significant advantage not available in standard OMP implementations where sequential processing is typically employed. The algorithm structure allows for efficient memory management through controlled batch sizes, preventing large matrix operations that typically cause memory bottlenecks. In summary, Batch-OMP represents a highly efficient algorithm that maintains performance quality while significantly reducing memory requirements through its batched processing methodology and parallelization features.