# Maxima and minima

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 (the *global* or *absolute* extrema).^{[1]}^{[2]}^{[3]} 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.

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 *ε* > 0 such that *f*(*x*^{∗}) ≥ *f*(*x*) for all *x* in *X* within distance *ε* of *x*^{∗}. Similarly, the function has a **local minimum point** at *x*^{∗}, if *f*(*x*^{∗}) ≤ *f*(*x*) for all *x* in *X* within distance *ε* 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. Mathematically, the given definition is written as follows:

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 *ε* > 0 such that, for all *x* in *X* within distance *ε* 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 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.

Likely the most important, yet quite obvious, feature of continuous real-valued functions of a real variable is that they decrease before local minima and increase afterwards, likewise for maxima. (Formally, if *f* is continuous real-valued function of a real variable *x*, then *x*_{0} is a local minimum if and only if there exist *a* < *x*_{0} < *b* such that *f* decreases on (*a*, *x*_{0}) and increases on (*x*_{0}, *b*))^{[4]} A direct consequence of this is the Fermat's theorem, which states that local extrema must occur at critical points (or points where the function is non-differentiable).^{[5]} 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.^{[6]}

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).

For functions of more than one variable, similar conditions apply. For example, in the (enlargeable) figure on 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 contradiction). In two and more dimensions, this argument fails. This is illustrated by the function

whose only critical point is at (0,0), which is a local minimum with *f*(0,0) = 0. However, it cannot be a global one, because *f*(2,3) = −5.

If the domain of a function for which an extremum is to be found consists itself of functions (i.e. if an extremum is to be found of a functional), then the extremum is found using the calculus of variations.

In the case of a general partial order, the **least element** (i.e., one that is smaller than all others) should not be confused with a **minimal element** (nothing is smaller). Likewise, a **greatest element** of a partially ordered set (poset) is an upper bound of the set which is contained within the set, whereas a **maximal element** *m* of a poset *A* is an element of *A* such that if *m* ≤ *b* (for any *b* in *A*), then *m* = *b*. Any least element or greatest element of a poset is unique, but a poset can have several minimal or maximal elements. If a poset has more than one maximal element, then these elements will not be mutually comparable.

In a totally ordered set, or *chain*, all elements are mutually comparable, so such a set can have at most one minimal element and at most one maximal element. Then, due to mutual comparability, the minimal element will also be the least element, and the maximal element will also be the greatest element. Thus in a totally ordered set, we can simply use the terms * minimum* and

*.*

**maximum**If a chain is finite, then it will always have a maximum and a minimum. If a chain is infinite, then it need not have a maximum or a minimum. For example, the set of natural numbers has no maximum, though it has a minimum. If an infinite chain *S* is bounded, then the closure *Cl*(*S*) of the set occasionally has a minimum and a maximum, in which case they are called the **greatest lower bound** and the **least upper bound** of the set *S*, respectively.