Multidimension Application Introduction and the Gradient


Many optimization applications have two or more decision variables. The previous chapters focused on univariate, one-DV, one-dimensional (1-D) applications. Although N-DV applications are more frequently encountered, univariate searches are important, and they reveal key issues in relatively uncomplicated situations. There are new issues and techniques associated with 2-D and higher-dimension applications, and this chapter begins a section related to 2-D, two-decision variable, and N-D applications. Each section of this book on optimization algorithms introduces techniques with 2-D examples and then extends the optimization techniques to N-D applications. In 2-D, we still have the generic optimization concept for maximization.

7.2Illustration of Surface and Terms
7.3Some Surface Analysis
7.4Parametric Notation
7.5Extension to Higher Dimension

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