Throughout this chapter, we only treat the minimization problem for convex functions (see Definition 24.23). Furthermore, in most cases, we assume that the objective function being minimized is a quadratic function. These minimization assumptions may easily cover the cases where a function needs to be maximized. In the case of concave functions (Definition 24.25), where we are interested in the maxima, the function may be multiplied by -1 which inverts it into a convex function such that the location of the maximum now points to the minimum of the new function. So, the maximization function is changed to a minimization function.
KeywordsObjective Function Nonlinear Optimization Inequality Constraint Line Search Hessian Matrix
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