Semi-continuity

In mathematical analysis, semicontinuity (or semi-continuity) is a property of extended real-valued functions that is weaker than continuity. An extended real-valued function $f$ is upper (respectively, lower) semicontinuous at a point $x_{0}$ if, roughly speaking, the function values for arguments near $x_{0}$ are not much higher (respectively, lower) than $f\left(x_{0}\right).$

A function is continuous if and only if it is both upper and lower semicontinuous. If we take a continuous function and increase its value at a certain point $x_{0}$ to $f\left(x_{0}\right)+c$ for some $c>0$ , then the result is upper semicontinuous; if we decrease its value to $f\left(x_{0}\right)-c$ then the result is lower semicontinuous.

The notion of upper and lower semicontinuous function was first introduced and studied by René Baire in his thesis in 1899.^[1]

Definitions

Assume throughout that $X$ is a topological space and $f:X\to {\overline {\mathbb {R} }}$ is a function with values in the extended real numbers ${\overline {\mathbb {R} }}=\mathbb {R} \cup \{-\infty ,\infty \}=[-\infty ,\infty ]$ .

Upper semicontinuity

A function $f:X\to {\overline {\mathbb {R} }}$ is called upper semicontinuous at a point $x_{0}\in X$ if for every real $y>f\left(x_{0}\right)$ there exists a neighborhood $U$ of $x_{0}$ such that $f(x)<y$ for all $x\in U$ .^[2] Equivalently, $f$ is upper semicontinuous at $x_{0}$ if and only if $\limsup _{x\to x_{0}}f(x)\leq f(x_{0})$ where lim sup is the limit superior of the function $f$ at the point $x_{0}.$

If $X$ is a metric space with distance function $d$ and $f(x_{0})\in \mathbb {R} ,$ this can also be restated using an $\varepsilon$ - $\delta$ formulation, similar to the definition of continuous function. Namely, for each $\varepsilon >0$ there is a $\delta >0$ such that $f(x)<f(x_{0})+\varepsilon$ whenever $d(x,x_{0})<\delta .$

A function $f:X\to {\overline {\mathbb {R} }}$ is called upper semicontinuous if it satisfies any of the following equivalent conditions:^[2]

(1) The function is upper semicontinuous at every point of its domain.

(2) For each

y\in \mathbb {R}

, the set

f^{-1}([-\infty ,y))=\{x\in X:f(x)<y\}

is open in

X

, where

[-\infty ,y)=\{t\in {\overline {\mathbb {R} }}:t<y\}

.

(3) For each

y\in \mathbb {R}

, the

y

-superlevel set

f^{-1}([y,\infty ))=\{x\in X:f(x)\geq y\}

is closed in

X

.

(4) The hypograph

\{(x,t)\in X\times \mathbb {R} :t\leq f(x)\}

is closed in

X\times \mathbb {R}

.

(5) The function

f

is continuous when the codomain

{\overline {\mathbb {R} }}

is given the left order topology. This is just a restatement of condition (2) since the left order topology is generated by all the intervals

[-\infty ,y)

.

Lower semicontinuity

A function $f:X\to {\overline {\mathbb {R} }}$ is called lower semicontinuous at a point $x_{0}\in X$ if for every real $y<f\left(x_{0}\right)$ there exists a neighborhood $U$ of $x_{0}$ such that $f(x)>y$ for all $x\in U$ . Equivalently, $f$ is lower semicontinuous at $x_{0}$ if and only if $\liminf _{x\to x_{0}}f(x)\geq f(x_{0})$ where $\liminf$ is the limit inferior of the function $f$ at point $x_{0}.$

If $X$ is a metric space with distance function $d$ and $f(x_{0})\in \mathbb {R} ,$ this can also be restated as follows: For each $\varepsilon >0$ there is a $\delta >0$ such that $f(x)>f(x_{0})-\varepsilon$ whenever $d(x,x_{0})<\delta .$

A function $f:X\to {\overline {\mathbb {R} }}$ is called lower semicontinuous if it satisfies any of the following equivalent conditions:

(1) The function is lower semicontinuous at every point of its domain.

(2) For each

y\in \mathbb {R}

, the set

f^{-1}((y,\infty ])=\{x\in X:f(x)>y\}

is open in

X

, where

(y,\infty ]=\{t\in {\overline {\mathbb {R} }}:t>y\}

.

(3) For each

y\in \mathbb {R}

, the

y

-sublevel set

f^{-1}((-\infty ,y])=\{x\in X:f(x)\leq y\}

is closed in

X

.

(4) The epigraph

\{(x,t)\in X\times \mathbb {R} :t\geq f(x)\}

is closed in

X\times \mathbb {R}

.^[3]^: 207

(5) The function

f

is continuous when the codomain

{\overline {\mathbb {R} }}

is given the right order topology. This is just a restatement of condition (2) since the right order topology is generated by all the intervals

(y,\infty ]

.

Examples

Consider the function $f,$ piecewise defined by: $f(x)={\begin{cases}-1&{\mbox{if }}x<0,\\1&{\mbox{if }}x\geq 0\end{cases}}$ This function is upper semicontinuous at $x_{0}=0,$ but not lower semicontinuous.

The floor function $f(x)=\lfloor x\rfloor ,$ which returns the greatest integer less than or equal to a given real number $x,$ is everywhere upper semicontinuous. Similarly, the ceiling function $f(x)=\lceil x\rceil$ is lower semicontinuous.

Upper and lower semicontinuity bear no relation to continuity from the left or from the right for functions of a real variable. Semicontinuity is defined in terms of an ordering in the range of the functions, not in the domain.^[4] For example the function $f(x)={\begin{cases}\sin(1/x)&{\mbox{if }}x\neq 0,\\1&{\mbox{if }}x=0,\end{cases}}$ is upper semicontinuous at $x=0$ while the function limits from the left or right at zero do not even exist.

If $X=\mathbb {R} ^{n}$ is a Euclidean space (or more generally, a metric space) and $\Gamma =C([0,1],X)$ is the space of curves in $X$ (with the supremum distance $d_{\Gamma }(\alpha ,\beta )=\sup\{d_{X}(\alpha (t),\beta (t)):t\in [0,1]\}$ ), then the length functional $L:\Gamma \to [0,+\infty ],$ which assigns to each curve $\alpha$ its length $L(\alpha ),$ is lower semicontinuous.^[5] As an example, consider approximating the unit square diagonal by a staircase from below. The staircase always has length 2, while the diagonal line has only length ${\sqrt {2}}$ .

Let $(X,\mu )$ be a measure space and let $L^{+}(X,\mu )$ denote the set of positive measurable functions endowed with the topology of convergence in measure with respect to $\mu .$ Then by Fatou's lemma the integral, seen as an operator from $L^{+}(X,\mu )$ to $[-\infty ,+\infty ]$ is lower semicontinuous.

Tonelli's theorem in functional analysis characterizes the weak lower semicontinuity of nonlinear functionals on L^p spaces in terms of the convexity of another function.

Properties

Unless specified otherwise, all functions below are from a topological space $X$ to the extended real numbers ${\overline {\mathbb {R} }}=[-\infty ,\infty ].$ Several of the results hold for semicontinuity at a specific point, but for brevity they are only stated for semicontinuity over the whole domain.

A function $f:X\to {\overline {\mathbb {R} }}$ is continuous if and only if it is both upper and lower semicontinuous.
The characteristic function or indicator function of a set $A\subset X$ (defined by $\mathbf {1} _{A}(x)=1$ if $x\in A$ and $0$ if $x\notin A$ ) is upper semicontinuous if and only if $A$ is a closed set. It is lower semicontinuous if and only if $A$ is an open set.
In the field of convex analysis, the characteristic function of a set $A\subset X$ is defined differently, as $\chi _{A}(x)=0$ if $x\in A$ and $\chi _{A}(x)=\infty$ if $x\notin A$ . With that definition, the characteristic function of any closed set is lower semicontinuous, and the characteristic function of any open set is upper semicontinuous.

Binary Operations on Semicontinuous Functions

Let $f,g:X\to {\overline {\mathbb {R} }}$ .

If $f$ and $g$ are lower semicontinuous, then the sum $f+g$ is lower semicontinuous^[6] (provided the sum is well-defined, i.e., $f(x)+g(x)$ is not the indeterminate form $-\infty +\infty$ ). The same holds for upper semicontinuous functions.
If $f$ and $g$ are lower semicontinuous and non-negative, then the product function $fg$ is lower semicontinuous. The corresponding result holds for upper semicontinuous functions.
The function $f$ is lower semicontinuous if and only if $-f$ is upper semicontinuous.
If $f$ and $g$ are upper semicontinuous and $f$ is non-decreasing, then the composition $f\circ g$ is upper semicontinuous. On the other hand, if $f$ is not non-decreasing, then $f\circ g$ may not be upper semicontinuous.^[7]
If $f$ and $g$ are lower semicontinuous, their (pointwise) maximum and minimum (defined by $x\mapsto \max\{f(x),g(x)\}$ and $x\mapsto \min\{f(x),g(x)\}$ ) are also lower semicontinuous. Consequently, the set of all lower semicontinuous functions from $X$ to ${\overline {\mathbb {R} }}$ (or to $\mathbb {R}$ ) forms a lattice. The corresponding statements also hold for upper semicontinuous functions.

Optimization of Semicontinuous Functions

The (pointwise) supremum of an arbitrary family $(f_{i})_{i\in I}$ of lower semicontinuous functions $f_{i}:X\to {\overline {\mathbb {R} }}$ (defined by $f(x)=\sup\{f_{i}(x):i\in I\}$ ) is lower semicontinuous.^[8]

In particular, the limit of a monotone increasing sequence

f_{1}\leq f_{2}\leq f_{3}\leq \cdots

of continuous functions is lower semicontinuous. (The Theorem of Baire below provides a partial converse.) The limit function will only be lower semicontinuous in general, not continuous. An example is given by the functions

f_{n}(x)=1-(1-x)^{n}

defined for

x\in [0,1]

for

n=1,2,\ldots .

Likewise, the infimum of an arbitrary family of upper semicontinuous functions is upper semicontinuous. And the limit of a monotone decreasing sequence of continuous functions is upper semicontinuous.

If $C$ is a compact space (for instance a closed bounded interval $[a,b]$ ) and $f:C\to {\overline {\mathbb {R} }}$ is upper semicontinuous, then $f$ attains a maximum on $C.$ If $f$ is lower semicontinuous on $C,$ it attains a minimum on $C.$

(Proof for the upper semicontinuous case: By condition (5) in the definition,

f

is continuous when

{\overline {\mathbb {R} }}

is given the left order topology. So its image

f(C)

is compact in that topology. And the compact sets in that topology are exactly the sets with a maximum. For an alternative proof, see the article on the extreme value theorem.)

Other Properties

(Theorem of Baire)^{[note 1]} Let $X$ be a metric space. Every lower semicontinuous function $f:X\to {\overline {\mathbb {R} }}$ is the limit of a point-wise increasing sequence of extended real-valued continuous functions on $X.$ In particular, there exists a sequence $\{f_{i}\}$ of continuous functions $f_{i}:X\to {\overline {\mathbb {R} }}$ such that

f_{i}(x)\leq f_{i+1}(x)\quad \forall x\in X,\ \forall i=0,1,2,\dots

and

\lim _{i\to \infty }f_{i}(x)=f(x)\quad \forall x\in X.

If

f

does not take the value

-\infty

, the continuous functions can be taken to be real-valued.^[9]^[10]

Additionally, every upper semicontinuous function

f:X\to {\overline {\mathbb {R} }}

is the limit of a monotone decreasing sequence of extended real-valued continuous functions on

X;

if

f

does not take the value

\infty ,

the continuous functions can be taken to be real-valued.

Any upper semicontinuous function $f:X\to \mathbb {N}$ on an arbitrary topological space $X$ is locally constant on some dense open subset of $X.$

If the topological space $X$ is sequential, then $f:X\to \mathbb {R}$ is upper semi-continuous if and only if it is sequentially upper semi-continuous, that is, if for any $x\in X$ and any sequence $(x_{n})_{n}\subset X$ that converges towards $x$ , there holds $\limsup _{n\to \infty }f(x_{n})\leqslant f(x)$ . Equivalently, in a sequential space, $f$ is upper semicontinuous if and only if its superlevel sets $\{\,x\in X\,|\,f(x)\geqslant y\,\}$ are sequentially closed for all $y\in \mathbb {R}$ . In general, upper semicontinuous functions are sequentially upper semicontinuous, but the converse may be false.

Semicontinuity of Set-valued Functions

For set-valued functions, several concepts of semicontinuity have been defined, namely upper, lower, outer, and inner semicontinuity, as well as upper and lower hemicontinuity. A set-valued function $F$ from a set $A$ to a set $B$ is written $F:A\rightrightarrows B.$ For each $x\in A,$ the function $F$ defines a set $F(x)\subset B.$ The preimage of a set $S\subset B$ under $F$ is defined as $F^{-1}(S):=\{x\in A:F(x)\cap S\neq \varnothing \}.$ That is, $F^{-1}(S)$ is the set that contains every point $x$ in $A$ such that $F(x)$ is not disjoint from $S$ .^[11]

Upper and Lower Semicontinuity

A set-valued map $F:\mathbb {R} ^{m}\rightrightarrows \mathbb {R} ^{n}$ is upper semicontinuous at $x\in \mathbb {R} ^{m}$ if for every open set $U\subset \mathbb {R} ^{n}$ such that $F(x)\subset U$ , there exists a neighborhood $V$ of $x$ such that $F(V)\subset U.$ ^[11]^{: Def. 2.1}

A set-valued map $F:\mathbb {R} ^{m}\rightrightarrows \mathbb {R} ^{n}$ is lower semicontinuous at $x\in \mathbb {R} ^{m}$ if for every open set $U\subset \mathbb {R} ^{n}$ such that $x\in F^{-1}(U),$ there exists a neighborhood $V$ of $x$ such that $V\subset F^{-1}(U).$ ^[11]^{: Def. 2.2}

Upper and lower set-valued semicontinuity are also defined more generally for a set-valued maps between topological spaces by replacing $\mathbb {R} ^{m}$ and $\mathbb {R} ^{n}$ in the above definitions with arbitrary topological spaces.^[11]

Note, that there is not a direct correspondence between single-valued lower and upper semicontinuity and set-valued lower and upper semicontinuouty. An upper semicontinuous single-valued function is not necessarily upper semicontinuous when considered as a set-valued map.^[11]^: 18 For example, the function $f:\mathbb {R} \to \mathbb {R}$ defined by $f(x)={\begin{cases}-1&{\mbox{if }}x<0,\\1&{\mbox{if }}x\geq 0\end{cases}}$ is upper semicontinuous in the single-valued sense but the set-valued map $x\mapsto F(x):=\{f(x)\}$ is not upper semicontinuous in the set-valued sense.

Inner and Outer Semicontinuity

A set-valued function $F:\mathbb {R} ^{m}\rightrightarrows \mathbb {R} ^{n}$ is called inner semicontinuous at $x$ if for every $y\in F(x)$ and every convergent sequence $(x_{i})$ in $\mathbb {R} ^{m}$ such that $x_{i}\to x$ , there exists a sequence $(y_{i})$ in $\mathbb {R} ^{n}$ such that $y_{i}\to y$ and $y_{i}\in F\left(x_{i}\right)$ for all sufficiently large $i\in \mathbb {N} .$ ^[12]^{[note 2]}

A set-valued function $F:\mathbb {R} ^{m}\rightrightarrows \mathbb {R} ^{n}$ is called outer semicontinuous at $x$ if for every convergence sequence $(x_{i})$ in $\mathbb {R} ^{m}$ such that $x_{i}\to x$ and every convergent sequence $(y_{i})$ in $\mathbb {R} ^{n}$ such that $y_{i}\in F(x_{i})$ for each $i\in \mathbb {N} ,$ the sequence $(y_{i})$ converges to a point in $F(x)$ (that is, $\lim _{i\to \infty }y_{i}\in F(x)$ ).^[12]

Notes

^ The result was proved by René Baire in 1904 for real-valued function defined on $\mathbb {R}$ . It was extended to metric spaces by Hans Hahn in 1917, and Hing Tong showed in 1952 that the most general class of spaces where the theorem holds is the class of perfectly normal spaces. (See Engelking, Exercise 1.7.15(c), p. 62 for details and specific references.)
^ In particular, there exists $i_{0}\geq 0$ such that $y_{i}\in F(x_{i})$ for every natural number $i\geq i_{0},$ . The necessisty of only considering the tail of $y_{i}$ comes from the fact that for small values of $i,$ the set $F(x_{i})$ may be empty.

References

^ Verry, Matthieu. "Histoire des mathématiques - René Baire".
^ ^a ^b Stromberg, p. 132, Exercise 4
^ Kurdila, A. J., Zabarankin, M. (2005). "Convex Functional Analysis". Lower Semicontinuous Functionals. Systems & Control: Foundations & Applications (1st ed.). Birkhäuser-Verlag. pp. 205–219. doi:10.1007/3-7643-7357-1_7. ISBN 978-3-7643-2198-7.
^ Willard, p. 49, problem 7K
^ Giaquinta, Mariano (2007). Mathematical analysis : linear and metric structures and continuity. Giuseppe Modica (1 ed.). Boston: Birkhäuser. Theorem 11.3, p.396. ISBN 978-0-8176-4514-4. OCLC 213079540.
^ Puterman, Martin L. (2005). Markov Decision Processes Discrete Stochastic Dynamic Programming. Wiley-Interscience. pp. 602. ISBN 978-0-471-72782-8.
^ Moore, James C. (1999). Mathematical methods for economic theory. Berlin: Springer. p. 143. ISBN 9783540662358.
^ "To show that the supremum of any collection of lower semicontinuous functions is lower semicontinuous".
^ Stromberg, p. 132, Exercise 4(g)
^ "Show that lower semicontinuous function is the supremum of an increasing sequence of continuous functions".
^ ^a ^b ^c ^d ^e Freeman, R. A., Kokotović, P. (1996). Robust Nonlinear Control Design. Birkhäuser Boston. doi:10.1007/978-0-8176-4759-9. ISBN 978-0-8176-4758-2..
^ ^a ^b Goebel, R. K. (January 2024). "Set-Valued, Convex, and Nonsmooth Analysis in Dynamics and Control: An Introduction". Chapter 2: Set convergence and set-valued mappings. Other Titles in Applied Mathematics. Society for Industrial and Applied Mathematics. pp. 21–36. doi:10.1137/1.9781611977981.ch2. ISBN 978-1-61197-797-4.

Bibliography

Benesova, B.; Kruzik, M. (2017). "Weak Lower Semicontinuity of Integral Functionals and Applications". SIAM Review. 59 (4): 703–766. arXiv:1601.00390. doi:10.1137/16M1060947. S2CID 119668631.
Bourbaki, Nicolas (1998). Elements of Mathematics: General Topology, 1–4. Springer. ISBN 0-201-00636-7.
Bourbaki, Nicolas (1998). Elements of Mathematics: General Topology, 5–10. Springer. ISBN 3-540-64563-2.
Engelking, Ryszard (1989). General Topology. Heldermann Verlag, Berlin. ISBN 3-88538-006-4.
Gelbaum, Bernard R.; Olmsted, John M.H. (2003). Counterexamples in analysis. Dover Publications. ISBN 0-486-42875-3.
Hyers, Donald H.; Isac, George; Rassias, Themistocles M. (1997). Topics in nonlinear analysis & applications. World Scientific. ISBN 981-02-2534-2.
Stromberg, Karl (1981). Introduction to Classical Real Analysis. Wadsworth. ISBN 978-0-534-98012-2.
Willard, Stephen (2004) [1970]. General Topology. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-43479-7. OCLC 115240.
Zălinescu, Constantin (30 July 2002). Convex Analysis in General Vector Spaces. River Edge, N.J. London: World Scientific Publishing. ISBN 978-981-4488-15-0. MR 1921556. OCLC 285163112 – via Internet Archive.

[9] The result was proved by René Baire in 1904 for real-valued function defined on $\mathbb {R}$ . It was extended to metric spaces by Hans Hahn in 1917, and Hing Tong showed in 1952 that the most general class of spaces where the theorem holds is the class of perfectly normal spaces. (See Engelking, Exercise 1.7.15(c), p. 62 for details and specific references.)

[14] In particular, there exists $i_{0}\geq 0$ such that $y_{i}\in F(x_{i})$ for every natural number $i\geq i_{0},$ . The necessisty of only considering the tail of $y_{i}$ comes from the fact that for small values of $i,$ the set $F(x_{i})$ may be empty.

[1] Verry, Matthieu. "Histoire des mathématiques - René Baire".

[Stromberg-2] Stromberg, p. 132, Exercise 4

[Kurdila2005-3] Kurdila, A. J., Zabarankin, M. (2005). "Convex Functional Analysis". Lower Semicontinuous Functionals. Systems & Control: Foundations & Applications (1st ed.). Birkhäuser-Verlag. pp. 205–219. doi:10.1007/3-7643-7357-1_7. ISBN 978-3-7643-2198-7.

[4] Willard, p. 49, problem 7K

[5] Giaquinta, Mariano (2007). Mathematical analysis : linear and metric structures and continuity. Giuseppe Modica (1 ed.). Boston: Birkhäuser. Theorem 11.3, p.396. ISBN 978-0-8176-4514-4. OCLC 213079540.

[6] Puterman, Martin L. (2005). Markov Decision Processes Discrete Stochastic Dynamic Programming. Wiley-Interscience. pp. 602. ISBN 978-0-471-72782-8.

[7] Moore, James C. (1999). Mathematical methods for economic theory. Berlin: Springer. p. 143. ISBN 9783540662358.

[8] "To show that the supremum of any collection of lower semicontinuous functions is lower semicontinuous".

[10] Stromberg, p. 132, Exercise 4(g)

[11] "Show that lower semicontinuous function is the supremum of an increasing sequence of continuous functions".

[freemanRobust-12] Freeman, R. A., Kokotović, P. (1996). Robust Nonlinear Control Design. Birkhäuser Boston. doi:10.1007/978-0-8176-4759-9. ISBN 978-0-8176-4758-2..

[goebelSetvalued-13] Goebel, R. K. (January 2024). "Set-Valued, Convex, and Nonsmooth Analysis in Dynamics and Control: An Introduction". Chapter 2: Set convergence and set-valued mappings. Other Titles in Applied Mathematics. Society for Industrial and Applied Mathematics. pp. 21–36. doi:10.1137/1.9781611977981.ch2. ISBN 978-1-61197-797-4.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[note 1]

[9]

[10]

[11]

[12]

[note 2]

Convex analysis and variational analysis
Basic concepts	Convex combination Convex function Convex set
Topics (list)	Choquet theory Convex geometry Convex metric space Convex optimization Duality Lagrange multiplier Legendre transformation Locally convex topological vector space Simplex
Maps	Convex conjugate Concave (Closed K- Logarithmically Proper Pseudo- Quasi-) Convex function Invex function Legendre transformation Semi-continuity Subderivative
Main results (list)	Carathéodory's theorem Ekeland's variational principle Fenchel–Moreau theorem Fenchel-Young inequality Jensen's inequality Hermite–Hadamard inequality Krein–Milman theorem Mazur's lemma Shapley–Folkman lemma Robinson–Ursescu Simons Ursescu
Sets	Convex hull (Orthogonally, Pseudo-) Convex set Effective domain Epigraph Hypograph John ellipsoid Lens Radial set/Algebraic interior Zonotope
Series	Convex series related ((cs, lcs)-closed, (cs, bcs)-complete, (lower) ideally convex, (Hx), and (Hwx))
Duality	Dual system Duality gap Strong duality Weak duality
Applications and related	Convexity in economics

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Semi-continuity

Definitions

Upper semicontinuity

Lower semicontinuity

Examples

Properties

Binary Operations on Semicontinuous Functions

Optimization of Semicontinuous Functions

Other Properties

Semicontinuity of Set-valued Functions

Upper and Lower Semicontinuity

Inner and Outer Semicontinuity

See also

Notes

References

Bibliography