We have just published a preprint where we study the regularization paths of optimization problems with regularization terms. Regularization is used in many different areas of optimization when solutions are sought which not only minimize a given function, but also possess a certain degree of regularity. Popular applications are image denoising, sparse regression and machine learning. Since the choice of the regularization parameter is crucial but often difficult, path-following methods are used to approximate the entire regularization path, i.e., the set of all possible solutions for all regularization parameters. More details can be found in RIS, and the paper can be downloaded from the arXiv.
