Uma abordagem numérica para problemas de otimização no Ensino Médio

Abstract

This paper aims to discuss methods for determining extreme points of functions of a real variable. It seeks to extend the number of optimization problems we can solve in basic education beyond those modeled by quadratic functions. For this we make use of "Numerical Methods". In Chapter 1 we talked about the calculation of extreme quadratic functions. We comment about the way we teach this part of mathematics in high school and show an interesting procedure for calculating extremes of the quadratic function. This procedure is based on the observation that the abscissa of the extreme point is not changed by making a kind of translation of the parabola. In Chapter 2 we emphasize classical results of optimization theory of real functions. These results are normally covered in calculus courses and serve both to ensure that there are extremes of continuous functions in closed intervals and determine this value. The results are presented from the viewpoint of Di erential and Integral Calculus of a real variable. In chapter 3 we discuss two simple numerical methods that can be used in high school without major problems. The use of these methods in this work is strongly related to the fact that the functions discussed in optimization problems are (in general) continuous and unimodal in the range where the problem makes sense. In Chapter 4 we propose three optimization problems, in this case the functions involved are not quadratic. In this moment we want to show the strength of the numerical methods introduced in Chapter 3 in solving these problems. In particular, we chose the "Golden Section Method" to be applied in these problems believing that assimilation of this method is easier by students of high school than the "Method of Bisection". Finally, we believe that the implementation of the "Method of Golden Section" in a spreadsheet brings agility to the process and motivates students to learn about this kind of computational resource so important nowadays.