A new regularized quasi-Newton algorithm for unconstrained optimization

DOI10.1016/J.AMC.2015.02.032zbMath1390.90527OpenAlexW1997879275MaRDI QIDQ1636866

Y. Aharonov

Publication date: 7 June 2018

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2015.02.032

zbMATH Keywords

unconstrained optimizationregularizationquasi-Newton method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Newton-type methods (49M15) Numerical computation of solutions to systems of equations (65H10) Numerical methods based on nonlinear programming (49M37) Approximation algorithms (68W25) Implicit function theorems; global Newton methods on manifolds (58C15)

Related Items (3)

A new adaptive trust region algorithm for optimization problems ⋮ A new augmented Lagrangian method for equality constrained optimization with simple unconstrained subproblem ⋮ A new regularized quasi-Newton method for unconstrained optimization

Uses Software

• CUTEr
• SifDec
• HSL-VF05

Cites Work

• Unnamed Item
• Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
• Adaptive cubic regularisation methods for unconstrained optimization. II: Worst-case function- and derivative-evaluation complexity
• On the global convergence of trust region algorithms for unconstrained minimization
• A Family of Trust-Region-Based Algorithms for Unconstrained Minimization with Strong Global Convergence Properties
• Global Convergence of a Class of Trust Region Algorithms for Optimization with Simple Bounds
• Solving the Trust-Region Subproblem using the Lanczos Method
• CUTEr and SifDec

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