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Geometric Hahn Banach and Convex Separation
1 · Prerequisites
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Dimensional Normed Spaces and Riesz Lemma
- Foundations of the Real Numbers for Analysis
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Simple Field Extensions and the Construction of the Complex Numbers
- Suprema and Infima
- The Analytic Hahn Banach Theorem
- The ZFC Axioms and the Basic Set Constructions
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
The gauge turns a translated open convex neighbourhood into a real sublinear functional. Hahn--Banach then supplies geometric separation, with real parts used throughout over complex scalars. The later results apply this separation to subspace closure, complemented finite-dimensional directions, hyperplanes, and the weak/norm closure theorem for convex sets.
3 · Logical flowchart
4 · Definitions, theorems and proofs
Absorbing, balanced, and absolutely convex sets
Definition
Let be a normed space over , with the scalar convention of Real and complex scalar conventions for normed spaces, and let . The set is absorbing if, for every , there is such that . It is balanced if for every with . It is absolutely convex if it is both convex and balanced. No closedness, openness, or positive definiteness is part of these definitions.
Minkowski functional of an absorbing set
Definition
For an absorbing subset of a normed space , its Minkowski functional or gauge is
The defining set is nonempty by absorption, so is finite and nonnegative. It is not asserted to be a norm or a seminorm: those conclusions need additional hypotheses on .
The gauge of a convex absorbing set is sublinear
Statement
If is convex and absorbing, then its gauge satisfies for and . Thus is a real sublinear functional.
Facts & Assumptions
Given: A convex absorbing set and .
The gauge is , and its defining set is nonempty (Minkowski functional of an absorbing set).
Proof
For , holds exactly when ; taking infima gives , while gives .
Absorption and convexity first give . Given and , choose , with , ; convexity with enlarges these to , for some . Then , hence .
Therefore for every such ; letting them decrease to the two infima proves subadditivity.
The gauge of an absolutely convex absorbing set is a seminorm
Statement
If is absolutely convex and absorbing, then for every scalar , and is a seminorm. It need not be positive definite.
Facts & Assumptions
Given: An absolutely convex absorbing , , and .
For convex absorbing , is nonnegative, subadditive, and homogeneous for nonnegative real scalars (The gauge of a convex absorbing set is sublinear).
Proof
If the assertion follows from [F1]. For , balancedness gives : one inclusion is balancedness and the reverse follows by applying it to the inverse scalar.
Hence exactly when , so taking infima yields . Together with [F1] this is precisely the seminorm axioms.
An open convex neighbourhood is recovered from its gauge
Statement
If is open, convex, and , then is absorbing and
Facts & Assumptions
Given: An open convex containing .
The gauge is defined for absorbing sets by an infimum over positive dilates (Minkowski functional of an absorbing set).
Proof
Openness gives for some . For any , , so is absorbing and [F1] applies.
If , openness gives with ; hence .
If , choose with , say . Convexity and imply . Thus both inclusions hold.
Weak, strict, and strong separation
Definition
For nonempty , a nonzero weakly separates and if . It strictly separates them if for every , and strongly separates them if there are with for all . Over , ; over these real parts are essential, since complex values are not ordered.
Separate a point from an open convex set
Statement
Assume the Axiom of Choice. Let be a nonempty open convex subset of a real or complex normed space and let . Then a nonzero satisfies
Facts & Assumptions
Given: The Axiom of Choice, a nonempty open convex and .
If an open convex set contains , it equals the strict unit sublevel set of its gauge (An open convex neighbourhood is recovered from its gauge).
Assuming the Axiom of Choice, a real linear functional dominated by a sublinear functional on a linear subspace extends to the whole real vector space with the same domination (Hahn-Banach dominated extension theorem for real vector spaces).
A real linear functional on a complex space yields the complex-linear functional with real part (A complex linear functional is recovered from its real part by f(x)=u(x)-iu(ix)).
The gauge of a convex absorbing set is a real sublinear functional (The gauge of a convex absorbing set is sublinear).
Proof
Choose and put , . Then and is open, convex, contains , and ; by [F1], and for .
By [F1], is absorbing, and [F4] makes sublinear. On the real line define . For , ; for , . Thus [F2] gives a real linear on with and .
Choose with . For one has and , so taking infima gives . Domination applied to both and gives . In the real case take ; in the complex case take , which is continuous by this estimate and has real part by [F3].
For , [F1] and give . Since , is nonzero. Hence the stated strict separation holds.
Separation of disjoint convex sets when one is open
Statement
If are nonempty disjoint convex sets and is open, then there is a nonzero such that
Facts & Assumptions
Given: Nonempty disjoint convex sets , with open.
An exterior point and a nonempty open convex set admit a strict separating continuous functional (Separate a point from an open convex set).
Proof
The difference is open and convex, and because .
Apply [F1] to and . It supplies nonzero with for every .
For and , , so . This is the required strict separation.
Strong separation of a closed and a compact convex set
Statement
Let be disjoint nonempty convex sets, where is closed and is compact. Then they are strongly separated by a nonzero functional in .
Facts & Assumptions
Given: Disjoint nonempty convex , with closed and compact.
Distance to a fixed nonempty set is continuous (indeed -Lipschitz) (, so the distance to a fixed nonempty set is -Lipschitz).
A continuous real-valued function on a compact metric space attains its minimum (A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value).
Disjoint convex sets, one open, are strictly separated by a nonzero continuous functional (Separation of disjoint convex sets when one is open).
Proof
By [F1]--[F2], has a minimum on . It is positive: a zero minimum would put some in the closed set . Choose .
The thickening is open and convex and is disjoint from . Apply [F3] to to obtain nonzero with for .
For , take the supremum over in the inequalities from step 2.1. Since , this gives for every . Hence , a positive gap.
A closed convex set is an intersection of closed half-spaces
Statement
Every nonempty closed convex is the intersection of the closed affine half-spaces that contain .
Facts & Assumptions
Given: A nonempty closed convex set .
Disjoint convex sets with one open are strictly separated by a nonzero continuous functional (Separation of disjoint convex sets when one is open).
Proof
Since every closed affine half-space in the family contains , their intersection contains .
If , choose with . The set is open and convex and still omits ; [F1] separates it from .
The closed half-space obtained by taking a positive fraction of the separation gap contains and excludes . Thus every exterior point is excluded from the intersection, proving equality.
Continuous annihilator of a linear subspace
Definition
Let be a normed space and let be a linear subspace. Its continuous annihilator is
This is an annihilator in the topological dual , not in the unrestricted algebraic dual.
Geometric Hahn--Banach theorem for subspaces
Statement
For a linear subspace and , there is with .
Facts & Assumptions
Given: A subspace and .
exactly when is positive (The closure of a nonempty is , equals together with its limit points, and is the smallest closed superset).
A bounded functional on any subspace extends to the ambient normed space without increasing its norm (A bounded linear functional on an arbitrary subspace extends with the same norm, without assuming the subspace is closed).
Proof
By [F1], . On define ; the representation is unique because .
For , , while the case is immediate. Thus .
Extend by [F2] to . Then and , so as required.
The annihilator detects the closure of a subspace
Statement
For every linear subspace ,
Facts & Assumptions
Given: A linear subspace .
Every point outside is sent to by some continuous functional vanishing on (Geometric Hahn--Banach theorem for subspaces).
Proof
If and , continuity of and give . Thus lies in the intersection.
If , [F1] gives with , so is absent from the intersection.
The two inclusions prove the formula.
Density is characterized by a zero annihilator
Statement
A linear subspace is dense if and only if .
Facts & Assumptions
Given: A linear subspace .
Proof
If is dense, [F1] says every vanishes on , hence is zero.
If , the intersection in [F1] is , so and is dense.
Finite-dimensional subspaces are complemented
Statement
Every finite-dimensional linear subspace of a normed space is complemented in .
Facts & Assumptions
Given: A finite-dimensional subspace .
Relative to a fixed finite basis, every coordinate functional on a finite-dimensional normed space is bounded (A chosen algebraic basis identifies a finite-dimensional normed space with a coordinate space).
A bounded functional on a subspace extends norm-preservingly to the ambient normed space (A bounded linear functional on an arbitrary subspace extends with the same norm, without assuming the subspace is closed).
A subspace is complemented exactly when it is the range of a bounded projection (A closed subspace is complemented exactly when it is the range of a bounded projection).
Proof
Choose a basis of , and let be its coordinate maps. By [F1]--[F2], extend each to .
Define by . It is bounded, has range in , and for satisfies .
Thus and ; [F3] makes complemented.
Closed finite-codimensional subspaces are complemented
Statement
Every closed finite-codimensional linear subspace of a normed space is complemented in .
Facts & Assumptions
Given: A closed subspace with finite-dimensional .
The quotient seminorm is a norm exactly when the subspace is closed (The quotient seminorm is a norm exactly when the subspace is closed).
Coordinate functionals for a fixed basis of a finite-dimensional normed space are bounded (A chosen algebraic basis identifies a finite-dimensional normed space with a coordinate space).
The range of a bounded projection is complemented (A closed subspace is complemented exactly when it is the range of a bounded projection).
Proof
By [F1], is a normed finite-dimensional quotient. Choose a basis of , representatives , and bounded coordinate maps supplied by [F2].
The map , , is bounded and satisfies . Hence is bounded, , and .
By [F3], is complemented.
Linear hyperplane
Definition
Let be a vector space over a field . A linear hyperplane of is a linear subspace such that is finite-dimensional over and . This is algebraic codimension one; closedness is an additional topological property, not part of the definition.
Closed hyperplanes are kernels of nonzero functionals
Statement
A linear hyperplane is closed if and only if for some nonzero .
Facts & Assumptions
Given: A linear hyperplane .
A point outside the closure of a subspace is separated from it by a continuous functional vanishing on that subspace (Geometric Hahn--Banach theorem for subspaces).
Proof
Suppose is closed and choose . By [F1] there is with and . Thus .
Since has dimension one, a proper subspace containing cannot strictly contain . As , is proper; hence .
Conversely, if with nonzero, continuity makes closed. The induced nonzero map is injective and onto, so .
Mazur theorem: weak and norm closure agree for convex sets
Statement
For a convex subset of a normed space , its norm closure equals its weak closure. Here the weak topology is generated by the basic neighbourhoods of
for finitely many and some .
Facts & Assumptions
Given: A convex set and .
Disjoint convex sets, one open, are strictly separated by a nonzero continuous functional (Separation of disjoint convex sets when one is open).
Proof
Every is norm-continuous, so every displayed basic weak neighbourhood is norm-open. Thus every weak-open set is norm-open, and .
If , both closures are empty. Otherwise is nonempty; if , choose with . This latter set is open and convex, so [F1] yields with for , .
Taking suprema over gives . The weak neighbourhood therefore misses , hence misses .
Thus is not in the weak closure whenever it is not in , proving the reverse inclusion and equality.
5 · Examples, counterexamples and false statements
None yet.
Sources
- Gerald Teschl, Topics in Real and Functional Analysis, §5.1
- Gerald Teschl, Topics in Real and Functional Analysis, Lemma 5.1
- Theo Buehler and Dietmar Salamon, Functional Analysis, §2.3.3
- Gerald Teschl, Topics in Real and Functional Analysis, Theorems 5.2--5.3
- Theo Buehler and Dietmar Salamon, Functional Analysis, Theorem 2.41
- Gerald Teschl, Topics in Real and Functional Analysis, Corollary 5.4
- Theo Buehler and Dietmar Salamon, Functional Analysis, Exercise 2.51
- Theo Buehler and Dietmar Salamon, Functional Analysis, Definition 2.52
- Theo Buehler and Dietmar Salamon, Functional Analysis, Theorem 2.53
- Theo Buehler and Dietmar Salamon, Functional Analysis, Corollary 2.55
- Theo Buehler and Dietmar Salamon, Functional Analysis, Corollary 2.56
- Piotr Hajlasz, Functional Analysis, Theorem 10.17(a)
- Piotr Hajlasz, Functional Analysis, Theorem 10.17(b)
- Theo Buehler and Dietmar Salamon, Functional Analysis, Definition 2.46
- Theo Buehler and Dietmar Salamon, Functional Analysis, Exercise 2.47
- Gerald Teschl, Topics in Real and Functional Analysis, Theorem 5.11