Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning statistics. In most real-world scenarios, however, problems are often ill-posed or the underlying model suffers from overfitting, leading erroneous trivial solutions. This dealt with by adding extra constraints, known regularization. this paper, we ...