Maxima and minima

Local and global maxima and minima for cos(3?x)/x, 0.1? x ?1.1

In mathematical analysis, the maxima and minima (the respective plurals of maximum and minimum) of a function, known collectively as extrema (the plural of extremum), are the largest and smallest value of the function, either within a given range (the local or relative extrema) or on the entire domain of a function (the global or absolute extrema). Pierre de Fermat was one of the first mathematicians to propose a general technique, adequality, for finding the maxima and minima of functions.

As defined in set theory, the maximum and minimum of a set are the greatest and least elements in the set, respectively. Unbounded infinite sets, such as the set of real numbers, have no minimum or maximum.

Definition

A real-valued function f defined on a domain X has a global (or absolute) maximum point at x^? if f(x^?) ? f(x) for all x in X. Similarly, the function has a global (or absolute) minimum point at x^? if f(x^?) ? f(x) for all x in X. The value of the function at a maximum point is called the maximum value of the function and the value of the function at a minimum point is called the minimum value of the function.

If the domain X is a metric space then f is said to have a local (or relative) maximum point at the point x^? if there exists some ? > 0 such that f(x^?) ? f(x) for all x in X within distance ? of x^?. Similarly, the function has a local minimum point at x^? if f(x^?) ? f(x) for all x in X within distance ? of x^?. A similar definition can be used when X is a topological space, since the definition just given can be rephrased in terms of neighbourhoods.

In both the global and local cases, the concept of a strict extremum can be defined. For example, x^? is a strict global maximum point if, for all x in X with x ? x^?, we have f(x^?) > f(x), and x^? is a strict local maximum point if there exists some ? > 0 such that, for all x in X within distance ? of x^? with x ? x^?, we have f(x^?) > f(x). Note that a point is a strict global maximum point if and only if it is the unique global maximum point, and similarly for minimum points.

A continuous real-valued function with a compact domain always has a maximum point and a minimum point. An important example is a function whose domain is a closed (and bounded) interval of real numbers (see the graph above).

Finding functional maxima and minima

Finding global maxima and minima is the goal of mathematical optimization. If a function is continuous on a closed interval, then by the extreme value theorem global maxima and minima exist. Furthermore, a global maximum (or minimum) either must be a local maximum (or minimum) in the interior of the domain, or must lie on the boundary of the domain. So a method of finding a global maximum (or minimum) is to look at all the local maxima (or minima) in the interior, and also look at the maxima (or minima) of the points on the boundary, and take the largest (or smallest) one.

Local extrema of differentiable functions can be found by Fermat's theorem, which states that they must occur at critical points. One can distinguish whether a critical point is a local maximum or local minimum by using the first derivative test, second derivative test, or higher-order derivative test, given sufficient differentiability.

For any function that is defined piecewise, one finds a maximum (or minimum) by finding the maximum (or minimum) of each piece separately, and then seeing which one is largest (or smallest).

Examples

The global maximum of

{\sqrt{x}}

The function x² has a unique global minimum at x = 0.
The function x³ has no global minima or maxima. Although the first derivative (3x²) is 0 at x = 0, this is an inflection point.
The function ${\sqrt[{x}]{x}}$
The function x^?x has a unique global maximum over the positive real numbers at x = 1/e.
The function x³/3 ? x has first derivative x² ? 1 and second derivative 2x. Setting the first derivative to 0 and solving for x gives stationary points at ?1 and +1. From the sign of the second derivative we can see that ?1 is a local maximum and +1 is a local minimum. Note that this function has no global maximum or minimum.
The function |x| has a global minimum at x = 0 that cannot be found by taking derivatives, because the derivative does not exist at x = 0.
The function cos(x) has infinitely many global maxima at 0, ±2 $?$ , ±4 $?$ , ..., and infinitely many global minima at ±?, ±3?, ….
The function 2 cos(x) ? x has infinitely many local maxima and minima, but no global maximum or minimum.
The function cos(3 $?$ x)/x with 0.1 ? x ? 1.1 has a global maximum at x = 0.1 (a boundary), a global minimum near x = 0.3, a local maximum near x = 0.6, and a local minimum near x = 1.0. (See figure at top of page.)
The function x³ + 3x² ? 2x + 1 defined over the closed interval (segment) [?4,2] has a local and maximum at x = ?1??15/3, a local minimum at x = ?1+?15/3, a global maximum at x = 2 and a global minimum at x = ?4.

Functions of more than one variable

Peano surface, a counterexample to some criteria of local maxima of the 19th century

For functions of more than one variable, similar conditions apply. For example, in the (enlargeable) figure at the right, the necessary conditions for a local maximum are similar to those of a function with only one variable. The first partial derivatives as to z (the variable to be maximized) are zero at the maximum (the glowing dot on top in the figure). The second partial derivatives are negative. These are only necessary, not sufficient, conditions for a local maximum because of the possibility of a saddle point. For use of these conditions to solve for a maximum, the function z must also be differentiable throughout. The second partial derivative test can help classify the point as a relative maximum or relative minimum. In contrast, there are substantial differences between functions of one variable and functions of more than one variable in the identification of global extrema. For example, if a bounded differentiable function f defined on a closed interval in the real line has a single critical point, which is a local minimum, then it is also a global minimum (use the intermediate value theorem and Rolle's theorem to prove this by reductio ad absurdum). In two and more dimensions, this argument fails, as the function

f(x,y)=x^{2}+y^{2}(1-x)^{3},\qquad x,y\in \mathbb {R} ,

shows. Its only critical point is at (0,0), which is a local minimum with ƒ(0,0) = 0. However, it cannot be a global one, because ƒ(2,3) = ?5.

Multivariate functions

The global maximum is the point at the top

Counterexample: The red do

Release Date :

12:00am on Friday 29th June 2012

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