Lasso Problem

The Lasso problem, a type of regularized regression, aims to find sparse solutions by minimizing a loss function subject to a penalty term that encourages sparsity. Current research focuses on improving the efficiency and robustness of Lasso algorithms, including developing novel methods for parameter tuning (e.g., ALMA), addressing challenges in distributed and high-dimensional settings (e.g., ADMM, Safe Screening), and handling issues like outliers and heterogeneous data distributions. These advancements have significant implications for various fields, enhancing the accuracy and reliability of techniques in areas such as MRI reconstruction, machine learning, and causal inference.