期刊
OPTIMIZATION
卷 72, 期 11, 页码 2769-2792出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02331934.2022.2075746
关键词
Unconstrained optimization; nonmonotone line search; cubic regularization; global convergence
The current study introduces two modified adaptive cubic regularized (ARC) methods for solving unconstrained optimization problems. These methods combine ARC with the nonmonotone Armijo-type line search and calculate step lengths in different paths. The global convergence properties of the proposed methods are analyzed, and the results demonstrate their superior performance compared to other existing algorithms.
The current study provides two modified adaptive cubic regularized methods for the unconstrained optimization problems. The basis of these methods are based on a combination of the adaptive cubic regularized method (ARC) with the nonmonotone Armijo-type line search. The step lengths are calculated in different paths for the modified algorithms. Moreover, we analyse the global convergence properties for the proposed methods. The obtained results illustrate the capability of the modified algorithms compared to the other provided algorithms.
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