An improved OWL-QN method for solving sparse logistic regression problems

Abstract

Sparse logistic regression, is a class of important problems in machine learning, which has wide applications in cybernetics, management sciences and internet, etc. This paper proposes an Improved Orthant-Wise Limited-memory Quasi-Newton (IOWL-QN) method for sparse logistic regression problems. The proposed method adopts the well-known BB stepsize strategy to approximate the Hessian of the goal objective function, and constructs the quasi-Newton vectors based on the gradient of the goal objective function. Under mild conditions, global convergence of the proposed method is established. Numerical results are reported to illustrate that the proposed method is feasible and efficient.