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CorollaryStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-14
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Bauer maximum principle

Statement

Assume the Axiom of Choice. Let K be a nonempty compact convex subset of a locally convex Hausdorff real or complex topological vector space. Every upper-semicontinuous convex function f:KR attains its maximum at an extreme point of K.

Facts & Assumptions

Given: AC, a locally convex Hausdorff real or complex TVS X, a nonempty compact convex KX, and an upper-semicontinuous convex f:KR.

[F1]

Upper semicontinuity means that each superlevel {x:f(x)a} is closed (Upper semicontinuous real map on a topological space).

[F2]

A singleton is a face exactly when its point is extreme (Extreme point and face).

[F3]

Assuming HB, continuous dual functionals separate distinct points of a Hausdorff locally convex space by their real parts (The continuous dual separates points in a Hausdorff locally convex space).

[F4]

In a compact space, every closed family with the finite-intersection property has nonempty intersection (A space is compact exactly when every family of closed subsets with the finite intersection property has nonempty intersection).

[F5]

Under AC, every nonempty poset whose chains have upper bounds has a maximal element (Zorn's lemma).

[F6]

AC says every family of nonempty sets has a choice function (The Axiom of Choice).

[F7]

AC supplies Hahn–Banach dominated extension (Hahn-Banach dominated extension theorem for real vector spaces).

Proof

technique · Zorn's lemma on closed extremal subsets
1.1

For each yK put Ly={xK:f(x)f(y)}, which is closed by [F1]. Any finite subfamily has nonempty intersection: choose from its finite list an index at which the finitely many real values f(y) are largest, and the corresponding y belongs to every listed Ly; the empty finite intersection is K. Thus [F4] supplies zyKLy, so m:=f(z) is the maximum of f on K.

F1F4given
2.1

The maximizer set M={xK:f(x)=m}={xK:f(x)m} is nonempty and closed by [F1]. Call a subset SK K-extremal when (1t)x+tyS, for x,yK and 0<t<1, implies x,yS.

F1step 1.1construct
3.1

The set M is K-extremal. Indeed, if w=(1t)x+tyM with 0<t<1, convexity and maximality give m=f(w)(1t)f(x)+tf(y)m; positivity of both coefficients and f(x),f(y)m force f(x)=f(y)=m.

step 1.1step 2.1given
4.1

Let P be the nonempty closed K-extremal subsets of M, ordered by reverse inclusion. It is nonempty because MP.

step 2.1step 3.1
5.1

An empty chain has upper bound M. For a nonempty chain CP, every finite intersection is its inclusion-smallest listed member and hence nonempty. Because every member is closed in compact K, [F4] makes H=SCS nonempty and closed. If a strict convex combination lies in H, extremality in every S puts both endpoints in every S, so H is K-extremal. Thus HP and is an upper bound in the reverse-inclusion order.

F4step 4.1
6.1

By [F5], with AC declared in [F6], P has a maximal element M0, equivalently an inclusion-minimal nonempty closed K-extremal subset of M.

F5F6step 4.1step 5.1
7.1

Suppose p,qM0 are distinct. By [F7], AC supplies HB, so [F3] gives a continuous f0X for which u=Ref0 has u(p)u(q).

F3F7step 6.1
8.1

Apply the finite-intersection argument of step 1.1 to the continuous real function uM0: its superlevels in M0 are closed in K because M0 is closed and u is continuous, and the family indexed by yM0 has the finite-intersection property. Hence u has a maximum c on M0, and N={xM0:u(x)=c} is nonempty and closed in K. It is proper because u(p)u(q).

F4step 1.1step 6.1step 7.1
9.1

The set N is K-extremal: if (1t)x+tyN with x,yK and 0<t<1, extremality of M0 first gives x,yM0; linearity yields c=(1t)u(x)+tu(y) while u(x),u(y)c, so positivity forces u(x)=u(y)=c and x,yN. Thus NP is a proper subset of M0, contradicting minimality.

step 6.1step 8.1
10.1

Therefore M0={e} for some eM. Since this singleton is K-extremal, it satisfies the singleton face condition in [F2], so e is extreme in K; and eM gives f(e)=m=maxKf.

F2step 2.1step 6.1step 9.1
11.1

The preceding maximum and extremality argument constructs a nonempty closed extremal maximizer set, the Zorn argument produces a minimal one, and the separating-functional argument proves it is a singleton consisting of the required extreme maximizer.

step 1.1step 3.1step 6.1step 10.1

Remarks

The maximizer set need not be convex: for f(x)=x2 on [1,1] it is {1,1}. The proof therefore does not apply Krein–Milman to that set; it uses closed K-extremal subsets, exactly as the endpoint calculation above requires.

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