CHAPTER 6
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
6.1 Introduction
In this chapter we consider the optimization problem
The function f : 
n → that we wish to minimize is a real-valued function called the objective function or cost function. The vector x is an n-vector of independent variables: x = [x1, x2, …, xn]
n. The variables x1, …, xn are often referred to as decision variables. The set Ω is a subset of n called the constraint set or feasible set.
The optimization problem above can be viewed as a decision problem that involves finding the “best” vector x of the decision variables over all possible vectors in Ω. By the “best” vector we mean the one that results in the-smallest value of the objective function. This vector is called the minimizer of f over Ω. It is possible ...
Get An Introduction to Optimization, 4th Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
