Sparse Recovery

Sparse recovery focuses on efficiently reconstructing high-dimensional signals from limited measurements, leveraging the inherent sparsity (few non-zero elements) of the signal. Current research emphasizes developing faster and more robust algorithms, including iterative methods like Iterative Hard Thresholding and Iteratively Reweighted Least Squares, and exploring the use of deep learning architectures, particularly unfolded iterative algorithms and overparameterized models, to improve recovery accuracy and efficiency. These advancements have significant implications for various fields, including signal processing, machine learning, and biological data analysis, enabling efficient data acquisition and analysis in applications ranging from medical imaging to DNA sequence analysis.