A Graphical Global Optimization Framework for Parameter Estimation of Statistical Models with Nonconvex Regularization Functions

Optimization problems with norm-bounding constraints appear in various applications, from portfolio optimization to machine learning, feature selection, and beyond. A widely used variant of these problems relaxes the norm-bounding constraint through Lagrangian relaxation and moves it to the objective function as a form of penalty or regularization term. A challenging class of these models uses … Read more

Iteratively Reweighted Group Lasso based on Log-composite Regularization

This paper addresses supervised learning problems with structured sparsity, where subsets of model coefficients form distinct groups. We introduce a novel log-composite regularizer in a bi-criteria optimization problem together with a loss (e.g., least squares) in order to reconstruct the desired group sparsity structure. We develop an iteratively reweighted algorithm that solves the group LASSO … Read more