Direct Optimization: Problem discretization
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.
KeywordsDirect Optimization Sequential Quadratic Programming Method Dimensional Optimization Problem Dimensional Space Versus Fast Local Convergence
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