Sparse Vector

Sparse vectors, characterized by a small number of non-zero elements, are central to various fields, aiming to efficiently represent and process high-dimensional data while mitigating computational complexity and noise. Current research focuses on developing algorithms for efficiently computing and utilizing sparse vectors within diverse models, including support vector machines, deep neural networks, and transformers, often incorporating techniques like $\ell_1$ regularization and iterative hard thresholding. These advancements have significant implications for applications such as feature selection in machine learning, time series forecasting, and compressed sensing, leading to improved accuracy, efficiency, and robustness in various domains.