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Geometric Hahn Banach and Convex Separation

1 · Prerequisites

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

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Absorbing, balanced, and absolutely convex sets

Definition

Let X be a normed space over K{R,C}, with the scalar convention of Real and complex scalar conventions for normed spaces, and let CX. The set C is absorbing if, for every xX, there is t>0 such that xtC. It is balanced if λCC for every λK with λ1. It is absolutely convex if it is both convex and balanced. No closedness, openness, or positive definiteness is part of these definitions.

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Minkowski functional of an absorbing set

Definition

For an absorbing subset C of a normed space X, its Minkowski functional or gauge is

pC(x):=inf{t>0:xtC}(xX).

The defining set is nonempty by absorption, so pC(x) is finite and nonnegative. It is not asserted to be a norm or a seminorm: those conclusions need additional hypotheses on C.

LemmaStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

The gauge of a convex absorbing set is sublinear

Statement

If CX is convex and absorbing, then its gauge satisfies pC(rx)=rpC(x) for r0 and pC(x+y)pC(x)+pC(y). Thus pC is a real sublinear functional.

Facts & Assumptions

Given: A convex absorbing set CX and x,yX.

[F1]

The gauge is pC(z)=inf{t>0:ztC}, and its defining set is nonempty (Minkowski functional of an absorbing set).

Proof

technique · direct
1.1

For r>0, rxtC holds exactly when x(t/r)C; taking infima gives pC(rx)=rpC(x), while r=0 gives pC(0)=0.

F1givenalgebra
1.2

Absorption and convexity first give 0C. Given a>pC(x) and b>pC(y), choose s<a, t<b with xsC, ytC; convexity with 0 enlarges these to x=ac, y=bd for some c,dC. Then (ac+bd)/(a+b)C, hence x+y(a+b)C.

F1givenchoose
2.1

Therefore pC(x+y)a+b for every such a,b; letting them decrease to the two infima proves subadditivity.

step 1.2F1algebra
LemmaStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

The gauge of an absolutely convex absorbing set is a seminorm

Statement

If C is absolutely convex and absorbing, then pC(λx)=λpC(x) for every scalar λ, and pC is a seminorm. It need not be positive definite.

Facts & Assumptions

Given: An absolutely convex absorbing CX, xX, and λK.

[F1]

For convex absorbing C, pC is nonnegative, subadditive, and homogeneous for nonnegative real scalars (The gauge of a convex absorbing set is sublinear).

Proof

technique · direct
1.1

If λ=0 the assertion follows from [F1]. For λ0, balancedness gives (λ/λ)C=C: one inclusion is balancedness and the reverse follows by applying it to the inverse scalar.

F1givenalgebra
2.1

Hence λxtC exactly when x(t/λ)C, so taking infima yields pC(λx)=λpC(x). Together with [F1] this is precisely the seminorm axioms.

step 1.1F1algebra
LemmaStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

An open convex neighbourhood is recovered from its gauge

Statement

If UX is open, convex, and 0U, then U is absorbing and

U={xX:pU(x)<1}.

Facts & Assumptions

Given: An open convex UX containing 0.

[F1]

The gauge is defined for absorbing sets by an infimum over positive dilates (Minkowski functional of an absorbing set).

Proof

technique · direct
1.1

Openness gives B(0,r)U for some r>0. For any x, x(x/r+1)U, so U is absorbing and [F1] applies.

givenF1choose
2.1

If xU, openness gives ε>0 with (1+ε)xU; hence pU(x)(1+ε)1<1.

step 1.1F1given
3.1

If pU(x)<1, choose t<1 with xtU, say x=tu. Convexity and 0U imply tuU. Thus both inclusions hold.

step 1.1F1choose
DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Weak, strict, and strong separation

Definition

For nonempty A,BX, a nonzero fX weakly separates A and B if supaARef(a)infbBRef(b). It strictly separates them if Ref(a)<Ref(b) for every aA,bB, and strongly separates them if there are α<β with Ref(a)α<βRef(b) for all a,b. Over R, Ref=f; over C these real parts are essential, since complex values are not ordered.

TheoremStatement: Literature-sourcedProof: AI-generatedaudited 2026-09-06Open item page →

Separate a point from an open convex set

Statement

Assume the Axiom of Choice. Let U be a nonempty open convex subset of a real or complex normed space X and let x0U. Then a nonzero fX satisfies

Ref(u)<Ref(x0)(uU).

Facts & Assumptions

Given: The Axiom of Choice, a nonempty open convex U and x0U.

[F1]

If an open convex set contains 0, it equals the strict unit sublevel set of its gauge (An open convex neighbourhood is recovered from its gauge).

[F2]

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).

[F3]

A real linear functional u on a complex space yields the complex-linear functional u(x)iu(ix) with real part u (A complex linear functional is recovered from its real part by f(x)=u(x)-iu(ix)).

[F4]

The gauge of a convex absorbing set is a real sublinear functional (The gauge of a convex absorbing set is sublinear).

Proof

technique · direct
1.1

Choose u0U and put V=Uu0, y=x0u0. Then y0 and V is open, convex, contains 0, and yV; by [F1], pV(y)1 and pV(v)<1 for vV.

givenF1choose
2.1

By [F1], V is absorbing, and [F4] makes pV sublinear. On the real line Ry define g(ty)=t. For t0, g(ty)=ttpV(y)=pV(ty); for t<0, g(ty)<0pV(ty). Thus [F2] gives a real linear h on X with hpV and h(y)=1.

step 1.1F1F2F4construct
3.1

Choose r>0 with B(0,r)V. For t>z/r one has z/tV and z/tV, so taking infima gives pV(±z)z/r. Domination applied to both z and z gives h(z)z/r. In the real case take f=h; in the complex case take f(z)=h(z)ih(iz), which is continuous by this estimate and has real part h by [F3].

step 2.1F3given
4.1

For u=u0+vU, [F1] and hpV give Ref(u)Ref(u0)=h(v)<1=h(y)=Ref(x0)Ref(u0). Since h(y)=1, f is nonzero. Hence the stated strict separation holds.

step 1.1step 2.1step 3.1F1algebra
TheoremStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Separation of disjoint convex sets when one is open

Statement

If U,VX are nonempty disjoint convex sets and U is open, then there is a nonzero fX such that

Ref(u)<Ref(v)(uU,vV).

Facts & Assumptions

Given: Nonempty disjoint convex sets U,V, with U open.

[F1]

An exterior point and a nonempty open convex set admit a strict separating continuous functional (Separate a point from an open convex set).

Proof

technique · direct
1.1

The difference D=UV is open and convex, and 0D because UV=.

givenalgebra
2.1

Apply [F1] to D and 0. It supplies nonzero fX with Ref(d)<0 for every dD.

step 1.1F1
3.1

For uU and vV, uvD, so Ref(u)Ref(v)<0. This is the required strict separation.

step 2.1algebra
TheoremStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Strong separation of a closed and a compact convex set

Statement

Let C,KX be disjoint nonempty convex sets, where C is closed and K is compact. Then they are strongly separated by a nonzero functional in X.

Facts & Assumptions

Given: Disjoint nonempty convex C,K, with C closed and K compact.

[F2]

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).

[F3]

Disjoint convex sets, one open, are strictly separated by a nonzero continuous functional (Separation of disjoint convex sets when one is open).

Proof

technique · direct
1.1

By [F1]--[F2], d(k,C) has a minimum δ on K. It is positive: a zero minimum would put some kK in the closed set C. Choose 0<r<δ.

F1F2givenchoose
2.1

The thickening W=C+B(0,r) is open and convex and is disjoint from K. Apply [F3] to W,K to obtain nonzero f with Ref(w)<Ref(k) for wW,kK.

step 1.1F3
3.1

For cC, take the supremum over bB(0,r) in the inequalities from step 2.1. Since supb<rRef(b)=rf, this gives Ref(c)+rfRef(k) for every kK. Hence supCRef+rfinfKRef, a positive gap.

step 2.1algebra
CorollaryStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

A closed convex set is an intersection of closed half-spaces

Statement

Every nonempty closed convex CX is the intersection of the closed affine half-spaces that contain C.

Facts & Assumptions

Given: A nonempty closed convex set CX.

[F1]

Disjoint convex sets with one open are strictly separated by a nonzero continuous functional (Separation of disjoint convex sets when one is open).

Proof

technique · direct
1.1

Since every closed affine half-space in the family contains C, their intersection contains C.

given
1.2

If xC, choose r>0 with B(x,r)C=. The set C+B(0,r/2) is open and convex and still omits x; [F1] separates it from {x}.

givenF1choose
2.1

The closed half-space Hx={z:Ref(z)Ref(x)ε} obtained by taking a positive fraction of the separation gap contains C and excludes x. Thus every exterior point is excluded from the intersection, proving equality.

step 1.2algebra
DefinitionDefinition: Literature-sourcedProof: Not applicableaudited 2026-09-06Open item page →

Continuous annihilator of a linear subspace

Definition

Let X be a normed space and let MX be a linear subspace. Its continuous annihilator is

M:={fX:f(m)=0 for every mM}.

This is an annihilator in the topological dual X, not in the unrestricted algebraic dual.

TheoremStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Geometric Hahn--Banach theorem for subspaces

Statement

For a linear subspace MX and xM, there is fM with f(x)=1.

Facts & Assumptions

Given: A subspace MX and xM.

[F2]

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

technique · direct
1.1

By [F1], δ=dist(x,M)>0. On M+Kx define g(m+λx)=λ; the representation is unique because xM.

F1givenconstruct
2.1

For λ0, m+λx=λx+m/λλδ, while the case λ=0 is immediate. Thus g1/δ.

step 1.1F1algebra
3.1

Extend g by [F2] to fX. Then fM=0 and f(x)=1, so fM as required.

step 2.1F2
CorollaryStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

The annihilator detects the closure of a subspace

Statement

For every linear subspace MX,

M=fMkerf.

Facts & Assumptions

Given: A linear subspace MX.

[F1]

Every point outside M is sent to 1 by some continuous functional vanishing on M (Geometric Hahn--Banach theorem for subspaces).

Proof

technique · direct
1.1

If zM and fM, continuity of f and fM=0 give f(z)=0. Thus M lies in the intersection.

given
1.2

If zM, [F1] gives fM with f(z)=1, so z is absent from the intersection.

F1given
2.1

The two inclusions prove the formula.

step 1.1step 1.2
CorollaryStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Density is characterized by a zero annihilator

Statement

A linear subspace MX is dense if and only if M={0}.

Facts & Assumptions

Given: A linear subspace MX.

[F1]

M=fMkerf (The annihilator detects the closure of a subspace).

Proof

technique · direct
1.1

If M is dense, [F1] says every fM vanishes on X, hence is zero.

F1given
2.1

If M={0}, the intersection in [F1] is X, so M=X and M is dense.

F1given
CorollaryStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Finite-dimensional subspaces are complemented

Statement

Every finite-dimensional linear subspace M of a normed space X is complemented in X.

Facts & Assumptions

Given: A finite-dimensional subspace MX.

[F1]

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).

[F2]

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).

[F3]

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

technique · direct
1.1

Choose a basis e1,,en of M, and let ϕj:MK be its coordinate maps. By [F1]--[F2], extend each ϕj to fjX.

givenF1F2choose
2.1

Define P:XX by P(x)=j=1nfj(x)ej. It is bounded, has range in M, and for m=ajejM satisfies P(m)=m.

step 1.1algebra
3.1

Thus P2=P and ranP=M; [F3] makes M complemented.

step 2.1F3
CorollaryStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Closed finite-codimensional subspaces are complemented

Statement

Every closed finite-codimensional linear subspace M of a normed space X is complemented in X.

Facts & Assumptions

Given: A closed subspace MX with finite-dimensional X/M.

[F1]

The quotient seminorm is a norm exactly when the subspace is closed (The quotient seminorm is a norm exactly when the subspace is closed).

[F2]

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).

Proof

technique · direct
1.1

By [F1], Q=X/M is a normed finite-dimensional quotient. Choose a basis q1,,qn of Q, representatives xjX, and bounded coordinate maps ϕj supplied by [F2].

F1F2givenchoose
2.1

The map S:QX, S(q)=jϕj(q)xj, is bounded and satisfies πS=IQ. Hence P=IXSπ is bounded, P2=P, and ranP=M.

step 1.1algebra
3.1

By [F3], M is complemented.

step 2.1F3
DefinitionDefinition: Literature-sourcedProof: Not applicableaudited 2026-09-06Open item page →

Linear hyperplane

Definition

Let X be a vector space over a field F. A linear hyperplane of X is a linear subspace H such that X/H is finite-dimensional over F and dimF(X/H)=1. This is algebraic codimension one; closedness is an additional topological property, not part of the definition.

TheoremStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Closed hyperplanes are kernels of nonzero functionals

Statement

A linear hyperplane HX is closed if and only if H=kerf for some nonzero fX.

Facts & Assumptions

Given: A linear hyperplane HX.

[F1]

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

technique · direct
1.1

Suppose H is closed and choose xH. By [F1] there is fX with fH=0 and f(x)=1. Thus Hkerf.

givenF1choose
2.1

Since X/H has dimension one, a proper subspace containing H cannot strictly contain H. As f(x)=1, kerf is proper; hence kerf=H.

step 1.1given
3.1

Conversely, if H=kerf with fX nonzero, continuity makes H closed. The induced nonzero map X/HK is injective and onto, so dim(X/H)=1.

givenalgebra
TheoremStatement: Literature-sourcedProof: AI-generatedjudge pass (gpt-5.6-terra)audited 2026-09-06Open item page →

Mazur theorem: weak and norm closure agree for convex sets

Statement

For a convex subset C of a normed space X, its norm closure equals its weak closure. Here the weak topology is generated by the basic neighbourhoods of x

{y:fj(yx)<ε (1jn)}

for finitely many fjX and some ε>0.

Facts & Assumptions

Given: A convex set CX and D=C.

[F1]

Disjoint convex sets, one open, are strictly separated by a nonzero continuous functional (Separation of disjoint convex sets when one is open).

Proof

technique · direct
1.1

Every fX is norm-continuous, so every displayed basic weak neighbourhood is norm-open. Thus every weak-open set is norm-open, and DCweak.

given
1.2

If C=, both closures are empty. Otherwise D is nonempty; if xD, choose r>0 with xD+B(0,r). This latter set is open and convex, so [F1] yields f0 with Ref(d+b)<Ref(x) for dD, b<r.

givenF1choose
2.1

Taking suprema over bB(0,r) gives supdDRef(d)+rfRef(x). The weak neighbourhood {y:f(yx)<rf/2} therefore misses D, hence misses C.

step 1.2algebra
3.1

Thus x is not in the weak closure whenever it is not in D, proving the reverse inclusion and equality.

step 1.1step 2.1

5 · Examples, counterexamples and false statements

None yet.

Sources