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Journal of Applied Mathematics and Computing

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28-08-2021 | Original Research

Efficient regularized Newton-type algorithm for solving convex optimization problem

Authors: Shazia Javed, Areeba Khan

Published in: Journal of Applied Mathematics and Computing | Issue 4/2022

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Abstract

In this paper, the subspace properties of trust-region methods are employed to develop a regularized Newton-type (RNT) algorithm for solving convex optimization problem. The proposed RNT algorithm is analyzed for quadratic convergence under the local error bound conditions and global convergence for unconstrained convex optimization problems which may have singular Hessian at the solutions. Afterwards numerical results are presented to show the efficiency and robustness of the proposed algorithm in producing an optimal solution for the given problem.

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Title: Efficient regularized Newton-type algorithm for solving convex optimization problem
Authors: Shazia Javed
Areeba Khan
Publication date: 28-08-2021
Publisher: Springer Berlin Heidelberg
Published in: Journal of Applied Mathematics and Computing / Issue 4/2022
Print ISSN: 1598-5865
Electronic ISSN: 1865-2085
DOI: https://doi.org/10.1007/s12190-021-01620-y

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