Generalized-Hukuhara Subgradient Method for Optimization Problem with Interval-valued Functions and its Application in Lasso Problem

Abstract

In this study, a gH-subgradient technique is developed to obtain efficient solutions to the optimization problems with nonsmooth nonlinear convex interval-valued functions. The algorithmic implementation of the developed gH-subgradient technique is illustrated. As an application of the proposed gH-subgradient technique, an 1 penalized linear regression problem, known as a lasso problem, with interval-valued features is solved.