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A Direct Method for Parabolic PDE Constrained Optimization ProblemsDirect Optimization: Problem discretization

A Direct Method for Parabolic PDE Constrained Optimization Problems: Direct Optimization: Problem... [The goal of this chapter is to obtain a discretized version of OCP (2.6). We discuss a so-called direct approach and summarize its main advantages and disadvantages in Section 3.1 in comparison with alternative approaches. In Sections 3.2 and 3.3 we discretize OCP (2.6) in two steps. First we discretize in space and obtain a large-scale ODE constrained OCP which we then discretize in time to obtain a large-scale Nonlinear Programming Problem (NLP) presented in Section 3.5. The numerical solution of this NLP is the subject of Part II in this thesis.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Direct Method for Parabolic PDE Constrained Optimization ProblemsDirect Optimization: Problem discretization

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Publisher
Springer Fachmedien Wiesbaden
Copyright
© Springer Fachmedien Wiesbaden 2014
ISBN
978-3-658-04475-6
Pages
19 –26
DOI
10.1007/978-3-658-04476-3_3
Publisher site
See Chapter on Publisher Site

Abstract

[The goal of this chapter is to obtain a discretized version of OCP (2.6). We discuss a so-called direct approach and summarize its main advantages and disadvantages in Section 3.1 in comparison with alternative approaches. In Sections 3.2 and 3.3 we discretize OCP (2.6) in two steps. First we discretize in space and obtain a large-scale ODE constrained OCP which we then discretize in time to obtain a large-scale Nonlinear Programming Problem (NLP) presented in Section 3.5. The numerical solution of this NLP is the subject of Part II in this thesis.]

Published: Nov 30, 2013

Keywords: Direct Optimization; Sequential Quadratic Programming Method; Dimensional Optimization Problem; Dimensional Space Versus; Fast Local Convergence

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