Sparse recovery

Sparse recovery is a technique used to reconstruct a signal or data that has only a few significant components (non-zero elements) within a larger dataset. Imagine trying to find the important pieces of information hidden among many irrelevant ones. By leveraging the idea that the true signal is sparse, algorithms can accurately recover it using fewer measurements or data points than traditionally needed. This concept is useful in areas like compressed sensing, medical imaging, and data compression, enabling efficient reconstruction of essential details from limited or noisy data while ignoring the irrelevant parts.