A new modified trust region algorithm for solving unconstrained optimization problems

Authors

  • Mohammad Heydari
  • Tayebeh Dehghan Niri Yazd University
  • Seyed Mohammad Mehdi Hosseini Yazd University

DOI:

https://doi.org/10.30495/jme.v12i1.601

Keywords:

Regularized Newton method, Unconstrained optimization, Nonconvex, Trust-region method, Convergence analysis.

Abstract

Iterative methods for optimization can be classified into two categories:
line search methods and trust region methods.
In this paper, we propose a modified regularized Newton method without line search for minimizing nonconvex
functions whose Hessian matrix may be singular.
The proposed method is proved to converge globally if the Gradient and Hessian of the objective
function are Lipschitz continuous.
Moreover, we report numerical results that show that the proposed algorithm is competitive with the existing methods.

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Published

2018-06-29

Issue

Vol. 12

Section

Vol. 12, No. 4, (2018)

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