Title : ( An adaptive semismooth Newton method for approximately solving control-constrained elliptic optimal control problems )
Authors: Omid Solaymani Fard , Akbar Hashemi Borzabadi , Farhad Sarani ,
دانلود فایل برای اعضای دانشگاهAccess to full-text not allowed by authors
Abstract
The main goal of this paper is to study, analyse and prove the global convergence property of a nonmonotone semismooth Newton method. Based on this property, we aim to exploit an adaptive nonmonotone line search method and apply it for the approximate solution of elliptic optimal control problems with control constraints. To this end, we first convert the optimal control problem to a bound constrained optimization problem by using a finite-difference discretization scheme and a Newton–Cotes rule. Then we implement the adaptive nonmonotone semismooth Newton method to the resulting bound constrained optimization problem. We give some numerical examples to demonstrate the validity, efficiency and applicability of the presented method.
Keywords
, Optimal control, nonmonotone semismooth Newton method, control constraints, finite-difference discretization scheme, elliptic optimal control problems
@article{paperid:1080100,
author = {Solaymani Fard, Omid and اکبر هاشمی برزآبادی and فرهاد سارانی},
title = {An adaptive semismooth Newton method for approximately solving control-constrained elliptic optimal control problems},
journal = {Transactions of the Institute of Measurement and Control},
year = {2019},
volume = {41},
number = {11},
month = {July},
issn = {0142-3312},
pages = {3010--3020},
numpages = {10},
keywords = {Optimal control; nonmonotone semismooth Newton method; control constraints; finite-difference discretization scheme; elliptic optimal control problems},
}
%0 Journal Article
%T An adaptive semismooth Newton method for approximately solving control-constrained elliptic optimal control problems
%A Solaymani Fard, Omid
%A اکبر هاشمی برزآبادی
%A فرهاد سارانی
%J Transactions of the Institute of Measurement and Control
%@ 0142-3312
%D 2019
