Sparse Solution

Sparse solutions, aiming to find solutions with minimal non-zero elements, are a central theme in various fields, driven by the need for efficient computation, improved interpretability, and enhanced robustness. Current research focuses on developing efficient algorithms, such as iterative hard thresholding and greedy coordinate descent, and incorporating sparsity constraints into diverse models, including neural networks and matrix factorization techniques. These advancements are impacting diverse applications, from feature selection and signal processing to deep learning and high-dimensional data analysis, by enabling more efficient and interpretable models.