University of Guilan Journal of Mathematical Modeling 2345-394X 11 2 2023 07 01 A new version of augmented self-scaling BFGS method 323 342 6533 10.22124/jmm.2023.23425.2089 EN Mohamad Jourak Department of Mathematics, Payame Noor University, P.O. Box. 19395-3697, Tehran, Iran Saeed Nezhadhosein Department of Mathematics, Payame Noor University, P.O. Box. 19395-3697, Tehran, Iran Farzad Rahpeymaii Department of Mathematics, Technical and Vocational University (TVU), Tehran, Iran Journal Article 2022 12 10 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. https://jmm.guilan.ac.ir/article_6533_5e2bdae9bbc316cde5a82ba4fd3c6148.pdf