Alphabeta Math
LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Continuity, sublinearity and strict sublevels of an open convex gauge

Statement

Let U be an open convex zero-neighborhood in a real or complex TVS. Its gauge p=pU is finite, nonnegative, subadditive, positively real-homogeneous and continuous. Moreover U={x:p(x)<1}. If U is balanced, then p(λx)=λp(x) for every scalar, so p is a continuous seminorm. It need not be positive definite.

Facts & Assumptions

Given: An open convex zero-neighborhood U and its gauge p.

[F1]

The gauge is the finite nonnegative infimum of admissible positive dilations, with p(0)=0 (Minkowski gauge for an open convex zero-neighborhood).

[F2]

An infimum is a greatest lower bound (Every nonempty set bounded below has an infimum).

[F3]

Orbit maps are continuous and nonzero dilations and translations are homeomorphisms (Translations, dilations and absorption in a topological vector space).

[F4]

Convexity, balance and seminorms use the conventions of Local convexity, convex and balanced sets, and the continuous dual.

[F5]

Sublinearity means subadditivity and homogeneity for nonnegative real scalars (A sublinear functional on a real vector space).

Proof

1.1

Write Sx={t>0:xtU}. If tSx and at, then x/a=(t/a)(x/t)+(1t/a)0U, so aSx. If a>p(x), it cannot be a lower bound of Sx; hence some tSx satisfies t<a, and then aSx. This uses only the defining greatest-lower-bound property, not an assumption that the infimum is attained.

F1F2F4
2.1

For r>0, Srx=rSx by direct substitution, so p(rx)=rp(x): multiplication by r bijects lower bounds of Sx with lower bounds of rSx and preserves their order. At r=0, both sides are zero. For η>0 put a=p(x)+η and b=p(y)+η. These are positive admissible numbers, and (x+y)/(a+b)=aa+b(x/a)+ba+b(y/b)U. Thus p(x+y)p(x)+p(y)+2η for every η>0. If subadditivity failed by a positive gap d, take η=d/4 to contradict this bound. Hence p is sublinear.

F1F2F4F5step 1.1
2.2

If p(x)<1, choose a with p(x)<a<1, for example (p(x)+1)/2. It is admissible, and convexity with zero gives xaUU. Conversely, if xU, continuity of ssx at s=1 and openness of U give an η>0 with (1+η)xU. Thus 1/(1+η)Sx, and p(x)1/(1+η)<1. These prove both inclusions of the strict-sublevel identity.

F1F3F4step 1.1
3.1

Given ε>0, the open zero-neighborhood N=ε(U(U)) has p(h)<ε and p(h)<ε for hN, by homogeneity and the strict-sublevel identity. Subadditivity gives p(x+h)p(x)p(h) and p(x)p(x+h)p(h), hence p(x+h)p(x)<ε. Translating N proves continuity at every x. This argument does not assert absolute domination by an asymmetric gauge.

F3step 2.1step 2.2
4.1

Suppose U is balanced. For a=1, balance gives aUU and a1UU, whence aU=U and Sax=Sx. For λ0, write λ=λa with a=1, and obtain p(λx)=λp(ax)=λp(x). At λ=0 this follows from p(0)=0. Thus the finite nonnegative continuous sublinear p is a seminorm.

F1F4step 2.1step 3.1
5.1

For concrete boundary calculations on the real line, U=(1,1) gives Sx=(x,) and p(x)=x, with S0=(0,). On R2 the open strip U={(x,y):x<1} gives pU(x,y)=x, since admissibility is exactly t>x; thus pU(0,1)=0 despite (0,1)0. These computations show why no positive-definiteness conclusion is available. All claimed properties are established without HB or AC.

F1step 2.2step 3.1step 4.1

Depends on

Used by

Dependency tree · two levels

22 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources