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Modified mesh adaptive direct search algorithm withapproximate Hessian matrix(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年02期
Page:
189-
Research Field:
Publishing date:

Info

Title:
Modified mesh adaptive direct search algorithm withapproximate Hessian matrix
Author(s):
Liu Mei1Liu Hongwei1Yang Shanxue2Liu Zexian13Lu Xiaoning1
1.School of Mathematics and Statistic,Xidian University,Xi’an 710126,China; 2.School of Statistics,Xi’an University of Finance and Economics,Xi’an 710100,China; 3.School of Mathematics and Computer Science,Hezhou University,Hezhou 542899,China
Keywords:
constrained optimization modified mesh adaptive direct search algorithm approximate Hessian matrix quadratic model funotions orthogonal triangular decomposition
PACS:
O221.2
DOI:
10.14177/j.cnki.32-1397n.2018.42.02.009
Abstract:
In order to solve the problem that the mesh adaptive direct search algorithm is easily failing into slow convergence and low efficiency when optimizing,the modified mesh adaptive direct search algorithm with the second directional derivative-based Hessian matrix is presented. The orthogonal triangular decomposition is applied to the presented algorithm. In search step,using the Taylor series expansion,the rank-one update and the linear regression,the quadratic model of the function with linear constrains is built by means of a second directional derivative-based Hessian matrix. By solving the subprogram of the problem,the local solution is obtained. The poll step searchs the best point according to the new poll directions in the neighbor of the trial points,and the update of the poll directions and some parameters is altered in this method. The modified algorithm outperforms the original algorithm in terms of iteration times on some test problems.

References:

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Last Update: 2018-04-30