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A new version of augmented self-scaling BFGS method | ||
| Journal of Mathematical Modeling | ||
| مقاله 7، دوره 11، شماره 2، مهر 2023، صفحه 323-342 اصل مقاله (1.82 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2023.23425.2089 | ||
| نویسندگان | ||
| Mohamad Jourak1؛ Saeed Nezhadhosein* 1؛ Farzad Rahpeymaii2 | ||
| 1Department of Mathematics, Payame Noor University, P.O. Box. 19395-3697, Tehran, Iran | ||
| 2Department of Mathematics, Technical and Vocational University (TVU), Tehran, Iran | ||
| چکیده | ||
| A new version of the augmented self-scaling memoryless BFGS quasi-Newton update, proposed in [Appl. Numer. Math. 167, 187--201, (2021)], is suggested for unconstrained optimization problems. To use the corresponding scaled parameter, the clustering of the eigenvalues of the approximate Hessian matrix about one point is applied with three approaches. The first and second approaches are based on the trace and the determinant of the matrix. The third approach is based on minimizing the measure function. The sufficient descent property is guaranteed for uniformly convex functions, and the global convergence of the proposed algorithm is proved both for the uniformly convex and general nonlinear objective functions, separately. Numerical experiments on a set of test functions of the CUTEr collection show that the proposed method is robust. In addition, the proposed algorithm is effectively applied to the salt and pepper noise elimination problem. | ||
| کلیدواژهها | ||
| Unconstrained optimization؛ augmented BFGS؛ noise elimination problem | ||
| مراجع | ||
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