scholarly journals New Approach for Secant Update Generalized Version of PSB

Author(s):  
N. Boutet ◽  
◽  
R. Haelterman ◽  
J. Degroote

Working with Quasi-Newton methods in optimization leads to one important challenge, being to find an estimate of the Hessian matrix as close as possible to the real matrix. While multisecant methods are regularly used to solve root finding problems, they have been little explored in optimization because the symmetry property of the Hessian matrix estimation is generally not compatible with the multisecant property. In this paper, we propose a solution to apply multisecant methods to optimization problems. Starting from the Powell-Symmetric-Broyden (PSB) update formula and adding pieces of information from the previous steps of the optimization path, we want to develop a new update formula for the estimate of the Hessian. A multisecant version of PSB is, however, generally mathematically impossible to build. For that reason, we provide a formula that satisfies the symmetry and is as close as possible to satisfy the multisecant condition and vice versa for a second formula. Subsequently, we add enforcement of the last secant equation to the symmetric formula and present a comparison between the different methods.

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 656
Author(s):  
Quan Qu ◽  
Xianfeng Ding ◽  
Xinyi Wang

In this paper, a new nonmonotone adaptive trust region algorithm is proposed for unconstrained optimization by combining a multidimensional filter and the Goldstein-type line search technique. A modified trust region ratio is presented which results in more reasonable consistency between the accurate model and the approximate model. When a trial step is rejected, we use a multidimensional filter to increase the likelihood that the trial step is accepted. If the trial step is still not successful with the filter, a nonmonotone Goldstein-type line search is used in the direction of the rejected trial step. The approximation of the Hessian matrix is updated by the modified Quasi-Newton formula (CBFGS). Under appropriate conditions, the proposed algorithm is globally convergent and superlinearly convergent. The new algorithm shows better performance in terms of the Dolan–Moré performance profile. Numerical results demonstrate the efficiency and robustness of the proposed algorithm for solving unconstrained optimization problems.


4OR ◽  
2017 ◽  
Vol 16 (3) ◽  
pp. 261-294 ◽  
Author(s):  
Vahid Morovati ◽  
Hadi Basirzadeh ◽  
Latif Pourkarimi

Author(s):  
Issam A. R. Moghrabi

This note focuses on developing quasi-Newton methods that combinem+1multistep and single-step updates on a single iteration for the sake of constructing the new approximation to the Hessian matrix to be used on the next iteration in computing the search direction. The approach considered here exploits the merits of the multistep methods and those of El-Baali (1999) to create a hybrid technique. Our numerical results are encouraging and reveal that our proposed approach is promising. The new methods compete well with El-Baali's extra update algorithms (1999).


2017 ◽  
Vol 67 (3) ◽  
pp. 443-487 ◽  
Author(s):  
Lorenzo Stella ◽  
Andreas Themelis ◽  
Panagiotis Patrinos

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