Limit superior and limit inferior

In mathematics, the limit inferior (also called infimum limit, liminf, inferior limit, lower limit, or inner limit) and limit superior (also called supremum limit, limsup, superior limit, upper limit, or outer limit) of a sequence can be thought of as limiting (i.e., eventual and extreme) bounds on the sequence. The limit inferior and limit superior of a function can be thought of in a similar fashion (see limit of a function). The limit inferior and limit superior of a set are the infimum and supremum of the set's limit points, respectively. In general, when there are multiple objects around which a sequence, function, or set accumulates, the inferior and superior limits extract the smallest and largest of them; the type of object and the measure of size is context-dependent, but the notion of extreme limits is invariant.

1 Definition for sequences
2 The case of sequences of real numbers
- 2.1 Interpretation
- 2.2 Properties
  - 2.2.1 Examples
3 Real-valued functions
4 Functions from metric spaces to metric spaces
5 Sequences of sets
6 Generalized definitions
- 6.1 Definition for a set
- 6.2 Definition for filter bases
  - 6.2.1 Specialization for sequences and nets
7 See also
8 References

Definition for sequences

The limit inferior of a sequence (x_n) is defined by

$\liminf_{n\to\infty}x_n�:= \lim_{n\to\infty}\Big(\inf_{m\geq n}x_m\Big)$

$\liminf_{n\to\infty}x_n�:= \sup_{n\geq 0}\,\inf_{m\geq n}x_m=\sup\{\,\inf\{\,x_m:m\geq n\,\}:n\geq 0\,\}.$

Similarly, the limit superior of (x_n) is defined by

$\limsup_{n\to\infty}x_n�:= \lim_{n\to\infty}\Big(\sup_{m\geq n}x_m\Big)$

$\limsup_{n\to\infty}x_n�:= \inf_{n\geq 0}\,\sup_{m\geq n}x_m=\inf\{\,\sup\{\,x_m:m\geq n\,\}:n\geq 0\,\}.$

Alternatively, the notations $\varliminf_{n\to\infty}x_n:=\liminf_{n\to\infty}x_n$ and $\varlimsup_{n\to\infty}x_n:=\limsup_{n\to\infty}x_n$ are sometimes used.

If the terms in the sequence are real numbers, the limit superior and limit inferior always exist, as real numbers or ±∞ (i.e., on the extended real number line). More generally, these definitions make sense in any partially ordered set, provided the suprema and infima exist, such as in a complete lattice.

Whenever the ordinary limit exists, the limit inferior and limit superior are both equal to it; therefore, each can be considered a generalization of the ordinary limit which is primarily interesting in cases where the limit does not exist. Whenever lim inf x_n and lim sup x_n both exist, we have

$\liminf_{n\to\infty}x_n\leq\limsup_{n\to\infty}x_n.$

Limits inferior/superior are related to big-O notation in that they bound a sequence only "in the limit"; the sequence may exceed the bound. However, with big-O notation the sequence can only exceed the bound in a finite prefix of the sequence, whereas the limit superior of a sequence like e^-n may actually be less than all elements of the sequence. The only promise made is that some tail of the sequence can be bounded by the limit superior (inferior) plus (minus) an arbitrarily small positive constant.

The limit superior and limit inferior of a sequence are a special case of those of a function (see below).

The case of sequences of real numbers

In mathematical analysis, limit superior and limit inferior are important tools for studying sequences of real numbers. Since the supremum and infimum of an unbounded set of real numbers may not exist (the reals are not a complete lattice), it is convenient to consider sequences in the affinely extended real number system: we add the positive and negative infinities to the real line to give the complete totally ordered set [-∞,∞], which is a complete lattice.

Interpretation

Consider a sequence $(x_n)$ consisting of real numbers. Assume that the limit superior and limit inferior are real numbers (so, not infinite).

The limit superior of $x_n$ is the smallest real number $b$ such that, for any positive real number $\varepsilon$ , there exists a natural number $N$ such that $x_n<b%2B\varepsilon$ for all $n>N$ . In other words, any number larger than the limit superior is an eventual upper bound for the sequence. Only a finite number of elements of the sequence are greater than $b%2B\varepsilon$ .
The limit inferior of $x_n$ is the largest real number $b$ that, for any positive real number $\varepsilon$ , there exists a natural number $N$ such that $x_n>b-\varepsilon$ for all $n>N$ . In other words, any number below the limit inferior is an eventual lower bound for the sequence. Only a finite number of elements of the sequence are less than $b-\varepsilon$ .

Properties

The relationship of limit inferior and limit superior for sequences of real numbers is as follows

$-\liminf_{n\to\infty} x_n = \limsup_{n\to\infty} (-x_n).$

As mentioned earlier, it is convenient to extend R to [−∞,∞]. Then, (x_n) in [−∞,∞] converges if and only if

$\liminf_{n\to\infty} x_n = \limsup_{n\to\infty} x_n,$

in which case $\lim_{n\to\infty} x_n$ is equal to their common value. (Note that when working just in R, convergence to −∞ or ∞ would not be considered as convergence.) Since the limit inferior is at most the limit superior, the condition

$\liminf_{n\to\infty} x_n = \infty$

implies that

$\lim_{n\to\infty} x_n = \infty,$

and the condition

$\limsup_{n\to\infty} x_n = - \infty$

implies that

$\lim_{n\to\infty} x_n = - \infty.$

$I = \liminf_{n\to\infty} x_n$

and

$S = \limsup_{n\to\infty} x_n,$

then the interval [I, S] need not contain any of the numbers x_n, but every slight enlargement [I − ε, S + ε] (for arbitrarily small ε > 0) will contain x_n for all but finitely many indices n. In fact, the interval [I, S] is the smallest closed interval with this property. We can formalize this property like this. If there exists a $\Lambda\in\mathbb{R}$ so that

$\Lambda<\limsup_{n\to\infty}x_n$

then there exists a subsequence $x_{k_n}$ of $x_n$ for which we have that

$x_{k_n}>\Lambda\quad \forall n.$

In the same way, an analogous property holds for the limit inferior: if

$\liminf_{n\to\infty}x_n<\lambda$

then there exists a subsequence $x_{k_n}$ of $x_n$ for which we have that

$x_{k_n}<\lambda\quad\forall n.$

On the other hand we have that if

$\limsup_{n\to\infty}x_n<\Lambda$

there exists a $n_0\in\mathbb{N}$ so that

$x_n<\Lambda\quad\forall n\geq n_0.$

Similarly, if there exists a $\lambda\in \mathbb{R}$ so that

$\lambda<\liminf_{n\to\infty}x_n$

there exists a $n_0\in\mathbb{N}$ so that

$x_n>\lambda\quad\forall n\geq n_0.$

To recapitulate:

If $\Lambda$ is greater than the limit superior, there are at most finitely many $x_n$ greater than $\Lambda$ ; if it is less, there are infinitely many.
If $\lambda$ is less than the limit inferior, there are at most finitely many $x_n$ less than $\lambda$ ; if it is greater, there are infinitely many.

In general we have that

$\inf_n x_n \leq \liminf_{n \to \infty} x_n \leq \limsup_{n \to \infty} x_n \leq \sup_n x_n$

The liminf and limsup of a sequence are respectively the smallest and greatest cluster points.

For any two sequences of real numbers $\{a_n\}, \{b_n\}$ , the limit superior satisfies subadditivity whenever the right side of the inequality is defined (i.e., not $\infty - \infty$ or $-\infty %2B \infty$ ):

$\limsup_{n \to \infty} (a_n %2B b_n) \leq \limsup_{n \to \infty}(a_n) %2B \limsup_{n \to \infty}(b_n).$ .

Analogously, the limit inferior satisfies superadditivity:

$\liminf_{n \to \infty} (a_n %2B b_n) \geq \liminf_{n \to \infty}(a_n) %2B \liminf_{n \to \infty}(b_n).$

In the particular case that one of the sequences actually converges, say $a_n \to a$ , then the inequalities above become equalities (with $\limsup_{n \to \infty}a_n$ or $\liminf_{n \to \infty}a_n$ being replaced by $a$ ).

Examples

As an example, consider the sequence given by x_n = sin(n). Using the fact that pi is irrational, one can show that

$\liminf_{n\to\infty} x_n = -1$

and

$\limsup_{n\to\infty} x_n = %2B1.$

(This is because the sequence {1,2,3,...} is equidistributed mod 2π, a consequence of the Equidistribution theorem.)

An example from number theory is

$\liminf_{n\to\infty}(p_{n%2B1}-p_n),$

where p_n is the n-th prime number. The value of this limit inferior is conjectured to be 2 - this is the twin prime conjecture - but as yet has not even been proved finite. The corresponding limit superior is $%2B\infty$ , because there are arbitrary gaps between consecutive primes.

Real-valued functions

Assume that a function is defined from a subset of the real numbers to the real numbers. As in the case for sequences, the limit inferior and limit superior are always well-defined if we allow the values +∞ and -∞; in fact, if both agree then the limit exists and is equal to their common value (again possibly including the infinities). For example, given f(x) = sin(1/x), we have lim sup_x→0 f(x) = 1 and lim inf_x→0 f(x) = -1. The difference between the two is a rough measure of how "wildly" the function oscillates, and in observation of this fact, it is called the oscillation of f at a. This idea of oscillation is sufficient to, for example, characterize Riemann-integrable functions as continuous except on a set of measure zero [1]. Note that points of nonzero oscillation (i.e., points at which f is "badly behaved") are discontinuities which, unless they make up a set of zero, are confined to a negligible set.

Functions from metric spaces to metric spaces

There is a notion of lim sup and lim inf for functions defined on a metric space whose relationship to limits of real-valued functions mirrors that of the relation between the lim sup, lim inf, and the limit of a real sequence. Take metric spaces X and Y, a subspace E contained in X, and a function f : E → Y. The space Y should also be an ordered set, so that the notions of supremum and infimum make sense. Define, for any limit point a of E,

$\limsup_{x \to a} f(x) = \lim_{\varepsilon \to 0} ( \sup \{ f(x)�: x \in E \cap B(a;\varepsilon) - \{a\} \} )$

and

$\liminf_{x \to a} f(x) = \lim_{\varepsilon \to 0} ( \inf \{ f(x)�: x \in E \cap B(a;\varepsilon) - \{a\} \} )$

where B(a;ε) denotes the metric ball of radius ε about a.

Note that as ε shrinks, the supremum of the function over the ball is monotone decreasing, so we have

$\limsup_{x\to a} f(x) = \inf_{\varepsilon > 0} (\sup \{ f(x)�: x \in E \cap B(a;\varepsilon) - \{a\} \})$

and similarly

$\liminf_{x\to a} f(x) = \sup_{\varepsilon > 0}(\inf \{ f(x)�: x \in E \cap B(a;\varepsilon) - \{a\} \}).$

This finally motivates the definitions for general topological spaces. Take X, Y, E and a as before, but now let X and Y both be topological spaces. In this case, we replace metric balls with neighborhoods:

$\limsup_{x\to a} f(x) = \inf \{ \sup \{ f(x)�: x \in E \cap U - \{a\} \}�: U\ \mathrm{open}, a \in U, E \cap U - \{a\} \neq \emptyset \}$

$\liminf_{x\to a} f(x) = \sup \{ \inf \{ f(x)�: x \in E \cap U - \{a\} \}�: U\ \mathrm{open}, a \in U, E \cap U - \{a\} \neq \emptyset \}$

(there is a way to write the formula using a lim using nets and the neighborhood filter). This version is often useful in discussions of semi-continuity which crop up in analysis quite often. An interesting note is that this version subsumes the sequential version by considering sequences as functions from the natural numbers as a topological subspace of the extended real line, into the space (the closure of N in [-∞, ∞] is N ∪ {∞}.)

Sequences of sets

The power set ℘(X) of a set X is a complete lattice that is ordered by set inclusion, and so the supremum and infimum of any set of sets, in terms of set inclusion, of subsets always exist. In particular, every subset Y of X is bounded above by X and below by the empty set ∅ because ∅ ⊆ Y ⊆ X. Hence, it is possible (and sometimes useful) to consider superior and inferior limits of sequences in ℘(X) (i.e., sequences of subsets of X).

There are two common ways to define the limit of sequences of set. In both cases:

The sequence accumulates around sets of points rather than single points themselves. That is, because each element of the sequence is itself a set, there exist accumulation sets that are somehow nearby to infinitely many elements of the sequence.
The supremum/superior/outer limit is a set that joins these accumulation sets together. That is, it is the union of all of the accumulation sets. When ordering by set inclusion, the supremum limit is the least upper bound on the set of accumulation points because it contains each of them. Hence, it is the supremum of the limit points.
The infimum/inferior/inner limit is a set where all of these accumulation sets meet. That is, it is the intersection of all of the accumulation sets. When ordering by set inclusion, the infimum limit is the greatest lower bound on the set of accumulation points because it is contained in each of them. Hence, it is the infimum of the limit points.
Because ordering is by set inclusion, then the outer limit will always contain the inner limit (i.e., lim inf X_n ⊆ lim sup X_n).

The difference between the two definitions involves the topology (i.e., how to quantify separation) is defined. In fact, the second definition is identical to the first when the discrete metric is used to induce the topology on X.

General set convergence

In this case, a sequence of sets approaches a limiting set when the elements of each member of the sequence approach the elements of the limiting set. In particular, if {X_n} is a sequence of subsets of X, then:

lim sup X_n, which is also called the outer limit, consists of those elements which are limits of points in X_n taken from (countably) infinitely many n. That is, x ∈ lim sup X_n if and only if there exists a sequence of points x_k and a subsequence {X_{n_k}} of {X_n} such that x_k ∈ X_{n_k} and x_k → x as k → ∞.
lim inf X_n, which is also called the inner limit, consists of those elements which are limits of points in X_n for all but finitely many n (i.e., cofinitely many n). That is, x ∈ lim inf X_n if and only if there exists a sequence of points {x_k} such that x_k ∈ X_k and x_k → x as k → ∞.

The limit lim X_n exists if and only if lim inf X_n and lim sup X_n agree, in which case lim X_n = lim sup X_n = lim inf X_n.^[1]

Special case: discrete metric

In this case, which is frequently used in measure theory, a sequence of sets approaches a limiting set when the limiting set includes elements from each of the members of the sequence. That is, this case specializes the first case when the topology on set X is induced from the discrete metric. For points x ∈ X and y ∈ X, the discrete metric is defined by

$d(x,y)�:= \begin{cases} 0 &\text{if } x = y,\\ 1 &\text{if } x \neq y. \end{cases}$

So a sequence of points {x_k} converges to point x ∈ X if and only if x_k = x for all but finitely many k. The following definition is the result of applying this metric to the general definition above.

If {X_n} is a sequence of subsets of X, then:

lim sup X_n consists of elements of X which belong to X_n for (countably) infinitely many values of n. That is, x ∈ lim sup X_n if and only if there exists a subsequence {X_{n_k}} of {X_n} such that x ∈ X_{n_k} for all k.
lim inf X_n consists of elements of X which belong to X_n for all but finitely many n (i.e., for cofinitely many n). That is, x ∈ lim inf X_n if and only if there exists some m>0 such that x ∈ X_n for all n>m.

The limit lim X exists if and only if lim inf X and lim sup X agree, in which case lim X = lim sup X = lim inf X.^[2] This definition of the inferior and superior limits is relatively strong because it requires that the elements of the extreme limits also be elements of each of the sets of the sequence.

Using the standard parlance of set theory, consider the infimum of a sequence of sets. The infimum is a greatest lower bound or meet of a set. In the case of a sequence of sets, the sequence constituents meet at an set that is somehow smaller than each constituent set. Set inclusion to provides an ordering that allows set intersection to generate a greatest lower bound ∩X_n of sets in the sequence {X_n}. Similarly, the supremum, which is the least upper bound or join, of a sequence of sets is the union ∪X_n of sets in sequence {X_n}. In this context, the inner limit lim inf X_n is the largest meeting of tails of the sequence, and the outer limit lim sup X_n is the smallest joining of tails of the sequence.

Let I_n be the meet of the n^th tail of the sequence. That is,

$I_n�:= \inf \{ X_m�: m \in \{n, n%2B1, n%2B2, \ldots\}\} = \bigcap_{m=n}^{\infty} X_m = X_n \cap X_{n%2B1} \cap X_{n%2B2} \cap \cdots.$

Then I_k ⊆ I_k+1 ⊆ I_k+2 because I_k+1 is the intersection of fewer sets than I_k. In particular, the sequence {I_k} is non-decreasing. So the inner/inferior limit is the least upper bound on this sequence of meets of tails. In particular,

$\begin{align} \liminf_{n\to\infty}X_n &:= \lim_{n\to\infty} \inf\{X_m: m \in \{n, n%2B1, \ldots\}\}\\ &= \sup\{\inf\{X_m: m \in \{n, n%2B1, \ldots\}\}: n \in \{1,2,\dots\}\}\\ &= {\bigcup_{n=1}^\infty}\left({\bigcap_{m=n}^\infty}X_m\right). \end{align}$

So the superior limit acts like a version of the standard supremum that is unaffected by set elements that occur only finitely many times. That is, the superemum limit is a set that is a superset (i.e., an upper bound) for all but finitely many elements.

Similarly, let J_m be the join of the m^th tail of the sequence. That is,

$J_m�:= \sup \{ X_m�: m \in \{n, n%2B1, n%2B2, \ldots\}\} = \bigcup_{m=n}^{\infty} X_m = X_n \cup X_{n%2B1} \cup X_{n%2B2} \cup \cdots.$

Then J_k ⊇ J_k+1 ⊇ J_k+2 because J_k+1 is the union of fewer sets than J_k. In particular, the sequence {J_k} is non-increasing. So the outer/superior limit is the greatest lower bound on this sequence of joins of tails. In particular,

$\begin{align} \limsup_{n\to\infty}X_n &:= \lim_{n\to\infty} \sup\{X_m: m \in \{n, n%2B1, \ldots\}\}\\ &= \inf\{\sup\{X_m: m \in \{n, n%2B1, \ldots\}\}: n \in \{1,2,\dots\}\}\\ &= {\bigcap_{n=1}^\infty}\left({\bigcup_{m=n}^\infty}X_m\right). \end{align}$

So the inferior limit acts like a version of the standard infimum that is unaffected by set elements that occur only finitely many times. That is, the infimum limit is a set that is a subset (i.e., a lower bound) for all but finitely many elements.

The limit lim X_n exists if and only if lim sup X_n=lim inf X_n, and in that case, lim X_n=lim inf X_n=lim sup X_n. In this sense, the sequence has a limit so long as all but finitely many of its elements are equal to the limit.

Examples

The following are several set convergence examples. They have been broken into sections with respect to the metric used to induce the topology on set X.

Using the discrete metric

The Borel–Cantelli lemma is an example application of these constructs.

Using either the discrete metric or the Euclidean metric

Consider the set X = {0,1} and the sequence of subsets:

$\{X_n\} = \{ \{0\},\{1\},\{0\},\{1\},\{0\},\{1\},\dots \}.$

The "odd" and "even" elements of this sequence form two subsequences, {{0},{0},{0},...} and {{1},{1},{1},...}, which have limit points 0 and 1, respectively, and so the outer or superior limit is the set {0,1} of these two points. However, there are no limit points that can be taken from the {X_n} sequence as a whole, and so the interior or inferior limit is the empty set {}. That is,

lim sup X_n = {0,1}
lim inf X_n = {}

However, for {Y_n} = {{0},{0},{0},...} and {Z_n} = {{1},{1},{1},...}:

lim sup Y_n = lim inf Y_n = lim Y_n = {0}
lim sup Z_n = lim inf Z_n = lim Z_n = {1}

Consider the set X = {50, 20, -100, -25, 0, 1} and the sequence of subsets:

$\{X_n\} = \{ \{50\}, \{20\}, \{-100\}, \{-25\}, \{0\},\{1\},\{0\},\{1\},\{0\},\{1\},\dots \}.$

As in the previous two examples,

lim sup X_n = {0,1}
lim inf X_n = {}

That is, the four elements that do not match the pattern do not affect the lim inf and lim sup because there are only finitely many of them. In fact, these elements could be placed anywhere in the sequence (e.g., at positions 100, 150, 275, and 55000). So long as the tails of the sequence are maintained, the outer and inner limits will be unchanged. The related concepts of essential inner and outer limits, which use the essential supremum and essential infimum, provide an important modification that "squashes" countably many (rather than just finitely many) interstitial additions.

Using the Euclidean metric

Consider the sequence of subsets of rational numbers:

$\{X_n\} = \{ \{0\},\{1\},\{1/2\},\{1/2\},\{2/3\},\{1/3\}, \{3/4\}, \{1/4\}, \dots \}.$

The "odd" and "even" elements of this sequence form two subsequences, {{0},{1/2},{2/3},{3/4},...} and {{1},{1/2},{1/3},{1/4},...}, which have limit points 1 and 0, respectively, and so the outer or superior limit is the set {0,1} of these two points. However, there are no limit points that can be taken from the {X_n} sequence as a whole, and so the interior or inferior limit is the empty set {}. So, as in the previous example,

lim sup X_n = {0,1}
lim inf X_n = {}

However, for {Y_n} = {{0},{1/2},{2/3},{3/4},...} and {Z_n} = {{1},{1/2},{1/3},{1/4},...}:

lim sup Y_n = lim inf Y_n = lim Y_n = {1}
lim sup Z_n = lim inf Z_n = lim Z_n = {0}

In each of these four cases, the elements of the limiting sets are not elements of any of the sets from the original sequence.

The Ω limit (i.e., limit set) of a solution to a dynamic system is the outer limit of solution trajectories of the system.^[1]^:50–51 Because trajectories become closer and closer to this limit set, the tails of these trajectories converge to the limit set.

For example, an LTI system that is the cascade connection of several stable systems with an undamped second-order LTI system (i.e., zero damping ratio) will oscillate endlessly after being perturbed (e.g., an ideal bell after being struck). Hence, if the position and velocity of this system are plotted against each other, trajectories will approach a circle in the state space. This circle, which is the Ω limit set of the system, is the outer limit of solution trajectories of the system. The circle represents the locus of a trajectory corresponding to a pure sinusoidal tone output; that is, the system output approaches/approximates a pure tone.

Generalized definitions

The above definitions are inadequate for many technical applications. In fact, the definitions above are specializations of the following definitions.

Definition for a set

The limit inferior of a set X ⊆ Y is the infimum of all of the limit points of the set. That is,

$\liminf X�:= \inf \{ x \in Y�: x \text{ is a limit point of } X \}\,$

Similarly, the limit superior of a set X is the supremum of all of the limit points of the set. That is,

$\limsup X�:= \sup \{ x \in Y�: x \text{ is a limit point of } X \}\,$

Note that the set X needs to be defined as a subset of a partially ordered set Y that is also a topological space in order for these definitions to make sense. Moreover, it has to be a complete lattice so that the suprema and infima always exist. In that case every set has a limit superior and a limit inferior. Also note that the limit inferior and the limit superior of a set do not have to be elements of the set.

Definition for filter bases

Take a topological space X and a filter base B in that space. The set of all cluster points for that filter base is given by

$\bigcap \{ \overline{B}_0�: B_0 \in B \}$

where $\overline{B}_0$ is the closure of $B_0$ . This is clearly a closed set and is similar to the set of limit points of a set. Assume that X is also a partially ordered set. The limit superior of the filter base B is defined as

$\limsup B�:= \sup \bigcap \{ \overline{B}_0�: B_0 \in B \}$

when that supremum exists. When X has a total order, is a complete lattice and has the order topology,

$\limsup B = \inf\{ \sup B_0�: B_0 \in B \}$

Proof: Similarly, the limit inferior of the filter base B is defined as

$\liminf B�:= \inf \bigcap \{ \overline{B}_0�: B_0 \in B \}$

when that infimum exists; if X is totally ordered, is a complete lattice, and has the order topology, then

$\liminf B = \sup\{ \inf B_0�: B_0 \in B \}$

If the limit inferior and limit superior agree, then there must be exactly one cluster point and the limit of the filter base is equal to this unique cluster point.

Specialization for sequences and nets

Note that filter bases are generalizations of nets, which are generalizations of sequences. Therefore, these definitions give the limit inferior and limit superior of any net (and thus any sequence) as well. For example, take topological space $X$ and the net $(x_\alpha)_{\alpha \in A}$ , where $(A,{\leq})$ is a directed set and $x_\alpha \in X$ for all $\alpha \in A$ . The filter base ("of tails") generated by this net is $B$ defined by

$B�:= \{ \{ x_\alpha�: \alpha_0 \leq \alpha \}�: \alpha_0 \in A \}.\,$

Therefore, the limit inferior and limit superior of the net are equal to the limit superior and limit inferior of $B$ respectively. Similarly, for topological space $X$ , take the sequence $(x_n)$ where $x_n \in X$ for any $n \in \mathbb{N}$ with $\mathbb{N}$ being the set of natural numbers. The filter base ("of tails") generated by this sequence is $C$ defined by

$C�:= \{ \{ x_n�: n_0 \leq n \}�: n_0 \in \mathbb{N} \}.\,$

Therefore, the limit inferior and limit superior of the sequence are equal to the limit superior and limit inferior of $C$ respectively.

References

In-line references

^ ^a ^b Goebel, Rafal; Sanfelice, Ricardo G.; Teel, Andrew R. (2009). "Hybrid dynamical systems". IEEE Control Systems Magazine 29 (2): 28–93. doi:10.1109/MCS.2008.931718.
^ Halmos, Paul R. (1950). Measure Theory. Princeton, NJ: D. Van Nostrand Company, Inc..

General references

Amann, H.; Escher, Joachim (2005). Analysis. Basel; Boston: Birkhäuser. ISBN 0817671536.

González, Mario O (1991). Classical complex analysis. New York: M. Dekker. ISBN 0824784154.

Limit superior and limit inferior

Contents

Definition for sequences

The case of sequences of real numbers

Interpretation

Properties

Examples

Real-valued functions

Functions from metric spaces to metric spaces

Sequences of sets

General set convergence

Special case: discrete metric

Examples

Generalized definitions

Definition for a set

Definition for filter bases

Specialization for sequences and nets

See also

References