In functional analysis, a branch of mathematics, the algebraic interior or radial kernel of a subset of a vector space is a refinement of the concept of the interior. It is the subset of points contained in a given set with respect to which it is absorbing, i.e. the radial points of the set.[1] The elements of the algebraic interior are often referred to as internal points.[2][3]

If M is a linear subspace of X and \( A\subseteq X \) then the algebraic interior ofΑ with respect to M is:[4]

\( {\displaystyle \operatorname {aint} _{M}A:=\left\{a\in X:\forall m\in M,\exists t_{m}>0{\text{ s.t. }}a+[0,t_{m}]\cdot m\subseteq A\right\}.}

where it is clear that \( {\displaystyle \operatorname {aint} _{M}A\subseteq A} \) and if \( {\displaystyle \operatorname {aint} _{M}A\neq \emptyset } \) then \( {\displaystyle M\subseteq \operatorname {aff} (A-A)} \) , where \( {\displaystyle \operatorname {aff} (A-A)} \) is the affine hull of \( {\displaystyle A-A} \) (which is equal to \( {\displaystyle \operatorname {span} (A-A)}). \)

Algebraic Interior (Core)

The set \( {\displaystyle \operatorname {aint} _{X}A} \) is called the algebraic interior of A or the core of A and it is denoted by \( {\displaystyle A^{i}} \) or \( {\displaystyle \operatorname {core} A} \) . Formally, if X is a vector space then the algebraic interior of \( A\subseteq X \) is

\( {\displaystyle \operatorname {aint} _{X}A:=\operatorname {core} (A):=\left\{a\in A:\forall x\in X,\exists t_{x}>0,\forall t\in [0,t_{x}],a+tx\in A\right\}.} \)[5]

If A is non-empty, then these additional subsets are also useful for the statements of many theorems in convex functional analysis (such as the Ursescu theorem):

\( {\displaystyle {}^{ic}A:={\begin{cases}{}^{i}A&{\text{ if }}\operatorname {aff} A{\text{ is a closed set,}}\\\emptyset &{\text{ otherwise}}\end{cases}}} \)

\( {\displaystyle {}^{ib}A:={\begin{cases}{}^{i}A&{\text{ if }}\operatorname {span} (A-a){\text{ is a barrelled linear subspace of }}X{\text{ for any/all }}a\in A{\text{,}}\\\emptyset &{\text{ otherwise}}\end{cases}}} \)

If X is a Fréchet space, A is convex, and \( {\displaystyle \operatorname {aff} A} \) is closed in X then \) {\displaystyle {}^{ic}A={}^{ib}A} \) but in general it's possible to have i c A = ∅ {\displaystyle {}^{ic}A=\emptyset } {\displaystyle {}^{ic}A=\emptyset } while \( {\displaystyle {}^{ib}A} \) is not empty.

Example

If \( {\displaystyle A=\{x\in \mathbb {R} ^{2}:x_{2}\geq x_{1}^{2}{\text{ or }}x_{2}\leq 0\}\subseteq \mathbb {R} ^{2}} \) then \( 0\in \operatorname {core}(A) \) , but \( 0\not \in \operatorname {int}(A) \) and \( 0\not \in \operatorname {core}(\operatorname {core}(A)) \).

Properties of core

If \( A,B\subset X \) then:

In general, \( (\operatorname {core} (A))} \operatorname {core}(A)\neq \operatorname {core}(\operatorname {core}(A)). \)

If A is a convex set then:

\( (\operatorname {core} (A))} \operatorname {core}(A)=\operatorname {core}(\operatorname {core}(A)) \), and

for all \( {\displaystyle x_{0}\in \operatorname {core} A,y\in A,0<\lambda \leq 1} \) then \( {\displaystyle \lambda x_{0}+(1-\lambda )y\in \operatorname {core} A} \)

Α is absorbing if and only if \( 0\in \operatorname {core}(A) \).[1]

\( A+\operatorname {core}B\subset \operatorname {core}(A+B) \)[6]

\( A+\operatorname {core}B=\operatorname {core}(A+B)\) if \( B=\operatorname {core}B \)[6]

Relation to interior

Let Χ be a topological vector space, \( \operatorname {int} \) denote the interior operator, and \( A\subset X \) then:

\( \operatorname {int}A\subseteq \operatorname {core}A \)

If Α is nonempty convex and Χ is finite-dimensional, then\( \operatorname {int}A=\operatorname {core}A \)[2]

If Α is convex with non-empty interior, then \( \operatorname {int}A=\operatorname {core}A \) [7]

If Α is a closed convex set and Χ is a complete metric space, then \( \operatorname {int}A=\operatorname {core}A \) [8]

Relative algebraic interior

If \( {\displaystyle M=\operatorname {aff} (A-A)} \) then the set \( {\displaystyle \operatorname {aint} _{M}A} \) is denoted by \( {\displaystyle {}^{i}A:=\operatorname {aint} _{\operatorname {aff} (A-A)}A} \) and it is called the relative algebraic interior ofΑ.[6] This name stems from the fact that \( {\displaystyle a\in A^{i}} \) if and only if aff \( {\displaystyle \operatorname {aff} A=X} \) and \( {\displaystyle a\in {}^{i}A} \) (where aff \( {\displaystyle \operatorname {aff} A=X} \) if and only if \( {\displaystyle \operatorname {aff} \left(A-A\right)=X}). \)

Relative interior

If A is a subset of a topological vector space X then the relative interior of A is the set

\( {\displaystyle \operatorname {rint} A:=\operatorname {int} _{\operatorname {aff} A}A}. \)

That is, it is the topological interior of A in \( {\displaystyle \operatorname {aff} A} \), which is the smallest affine linear subspace of X containing A. The following set is also useful:

\( {\displaystyle \operatorname {ri} A:={\begin{cases}\operatorname {rint} A&{\text{ if }}\operatorname {aff} A{\text{ is a closed subspace of }}X{\text{,}}\\\emptyset &{\text{ otherwise}}\end{cases}}} \)

Quasi relative interior

If A is a subset of a topological vector space X then the quasi relative interior of A is the set

\( {\displaystyle \operatorname {qri} A:=\left\{a\in A:{\overline {\operatorname {cone} }}(A-a){\text{ is a linear subspace of }}X\right\}}. \)

In a Hausdorff finite dimensional topological vector space,\( {\displaystyle \operatorname {qri} A={}^{i}A={}^{ic}A={}^{ib}A}. \)

See also

Bounding point

Interior (topology)

Quasi-relative interior

Relative interior

Order unit

Ursescu theorem

References

Jaschke, Stefan; Kuchler, Uwe (2000). "Coherent Risk Measures, Valuation Bounds, and ( μ , ρ )-Portfolio Optimization".

Aliprantis, C.D.; Border, K.C. (2007). Infinite Dimensional Analysis: A Hitchhiker's Guide (3rd ed.). Springer. pp. 199–200. doi:10.1007/3-540-29587-9. ISBN 978-3-540-32696-0.

John Cook (May 21, 1988). "Separation of Convex Sets in Linear Topological Spaces" (pdf). Retrieved November 14, 2012.

Zalinescu 2002, p. 2.

Nikolaĭ Kapitonovich Nikolʹskiĭ (1992). Functional analysis I: linear functional analysis. Springer. ISBN 978-3-540-50584-6.

Zălinescu, C. (2002). Convex analysis in general vector spaces. River Edge, NJ: World Scientific Publishing Co., Inc. pp. 2–3. ISBN 981-238-067-1. MR 1921556.

Shmuel Kantorovitz (2003). Introduction to Modern Analysis. Oxford University Press. p. 134. ISBN 9780198526568.

Bonnans, J. Frederic; Shapiro, Alexander (2000), Perturbation Analysis of Optimization Problems, Springer series in operations research, Springer, Remark 2.73, p. 56, ISBN 9780387987057.

Zalinescu, C. (2002). Convex Analysis in General Vector Spaces. World Scientific. ISBN 978-981-238-067-8.

vte

Functional analysis (topics – glossary)

Spaces

Hilbert space Banach space Fréchet space topological vector space

Theorems

Hahn–Banach theorem closed graph theorem uniform boundedness principle Kakutani fixed-point theorem Krein–Milman theorem min-max theorem Gelfand–Naimark theorem Banach–Alaoglu theorem

Operators

bounded operator compact operator adjoint operator unitary operator Hilbert–Schmidt operator trace class unbounded operator

Algebras

Banach algebra C*-algebra spectrum of a C*-algebra operator algebra group algebra of a locally compact group von Neumann algebra

Open problems

invariant subspace problem Mahler's conjecture

Applications

Besov space Hardy space spectral theory of ordinary differential equations heat kernel index theorem calculus of variation functional calculus integral operator Jones polynomial topological quantum field theory noncommutative geometry Riemann hypothesis

Advanced topics

locally convex space approximation property balanced set Schwartz space weak topology barrelled space Banach–Mazur distance Tomita–Takesaki theory

vte

Topological vector spaces (TVSs)

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Banach space Continuous linear operator Functionals Hilbert space Linear operators Locally convex space Homomorphism Topological vector space Vector space

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Closed graph theorem F. Riesz's theorem Hahn–Banach (hyperplane separation Vector-valued Hahn–Banach) Open mapping (Banach–Schauder) (Bounded inverse) Uniform boundedness (Banach–Steinhaus)

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Almost open Bilinear (form operator) and Sesquilinear forms Closed Compact operator Continuous and Discontinuous Linear maps Densely defined Homomorphism Functionals Norm Operator Seminorm Sublinear Transpose

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Set operations

Affine hull (Relative) Algebraic interior (core) Convex hull Linear span Minkowski addition Polar (Quasi) Relative interior

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Asplund B-complete/Ptak Banach (Countably) Barrelled (Ultra-) Bornological Brauner Complete (DF)-space Distinguished F-space Fréchet (tame Fréchet) Grothendieck Hilbert Infrabarreled Interpolation space LB-space LF-space Locally convex space Mackey (Pseudo)Metrizable Montel Quasibarrelled Quasi-complete Quasinormed (Polynomially Semi-) Reflexive Riesz Schwartz Semi-complete Smith Stereotype (B Strictly Uniformly convex (Quasi-) Ultrabarrelled Uniformly smooth Webbed With the approximation property

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