Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization

• Published in: Journal of inequalities and applications Vol. 2024; no. 1; pp. 155 - 17
• Main Authors: Lim, Keat Hee, Leong, Wah June
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 05.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Analysis; Applications of Mathematics; Approximation; Armijo line search; Control and Optimization in Sustainability Applications; Convergence; Iterative methods; Large-scale optimization; Least-change updating scheme; Mathematics; Mathematics and Statistics; Optimization; Quasi Newton methods; Quasi-Newton-type methods; Searching; Symmetry; Weak secant relations; Armijo line search; Quasi-Newton-type methods; Large-scale optimization; Least-change updating scheme; Weak secant relations

This article presents two variants of memoryless quasi-Newton methods with backtracking line search for large-scale unconstrained minimization. These updating methods are derived by means of a least-change updating strategy subjected to some weaker form of secant relation obtained by projecting the...