Generalized-Hukuhara-Gradient Efficient-Direction Method to Solve Optimization Problems with Interval-Valued Functions and Its Application in Least-Squares Problems

This article proposes a general gH-gradient efficient-direction method and $${\mathcal{W}}$$ -gH-gradient efficient for the optimization problems with interval-valued functions. The convergence analysis step-wise algorithms of both methods are presented. It is observed that converges linearly strongly convex objective function. To develop proposed to study their convergence, ideas strong convexity sequential criteria gH-continuity function illustrated. In sequel, new definition gH-differentiability functions also proposed. described help newly defined concept linear noticed superior existing ones. For gH-differentiable function, relation an optimality condition interval problem derived. derived condition, notion direction introduced. idea used gradient methods. As application methods, least-square data by solved. least square illustrated polynomial fitting logistic curve fitting.