Advertisement

Nonlinear Optimization

  • Homayoon Beigi
Chapter

Abstract

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.

Keywords

Objective Function Nonlinear Optimization Inequality Constraint Line Search Hessian Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Recognition Technologies, Inc.Yorktown HeightsUSA

Personalised recommendations