Reflexive midpoint convexity and diamond distortion

Proves a reflexive Banach space can be midpoint convex far out yet admit no asymptotically uniformly convex norm.

7 papers September 27, 2026 Explained by AI

Asymptotic convexity asks whether the unit ball keeps bulging once finitely many directions are ignored. A weaker, two-sided version tests opposite directions together. Earlier work separated the two notions, but only in a space that is not reflexive. The main paper builds a reflexive example and shows that diamond graphs cannot sit in it with bounded distortion. Lean checks the main results of all seven papers.

First step
2016
Dilworth, Kutzarova, Randrianarivony, Revalski and Zhivkov, who introduced asymptotic midpoint uniform convexity
Significance
Solid
It settles a recorded problem on diamond embeddings in reflexive spaces, with Lean checks, in a specialist corner of Banach space geometry.
Trust
Formal proof listed
Field
Functional analysis
7 papers

The explanation

Take the unit vectors x = (1, 0) and y = (0, 1) in the plane with ordinary length. The vectors x + y and x − y both have length √2, about 1.414. Their average length exceeds 1 by about 0.414. That gain is a sign the unit ball is round. Now use the max length, the larger of the two coordinates. Then x + y = (1, 1) has length exactly 1. There is no gain, because the unit ball is a square with flat sides.

A Banach space is a space of vectors with a length rule, a norm, where limits behave well. In infinite dimensions, asymptotic convexity asks for such gains when y is pushed far out. Far out means y avoids any finite set of directions you name. The norm is asymptotically uniformly convex, or AUC, if x + ty always gains length. It is asymptotically midpoint uniformly convex, or AMUC, if the average of x + ty and x − ty gains. AMUC was introduced in 2016, with a norm on the sequence space ℓ2 that is AMUC but not AUC. Baudier then showed that a space can have an AMUC norm but no equivalent AUC norm. An equivalent norm changes lengths by at most fixed factors. His example was not reflexive. Reflexive means the space equals its double dual, the space of linear measurements of its linear measurements.

The question also has a metric side. A countably branching diamond starts as one edge. At each step, every edge becomes countably many two-edge paths. Distortion measures how much an embedding must stretch some distances relative to others. An AMUC norm stops diamonds from embedding with one bound on distortion. For reflexive spaces with extra structure, failing to be AUC renormable gives such embeddings. The open problem was whether that holds for every reflexive space.

The main paper says no. It builds a separable reflexive space from norms on segments of a tree. The space has no equivalent AUC norm. Its midpoint gain at step t is at least √(1 + t²/12) − 1. At t = 1, that is about 0.0408. Every embedding of the depth-k diamond has distortion at least √(1 + k/12). For k = 12, that is √2, about 1.414. For k = 96, it is 3. For k = 1,200, it is about 10.05. So no single bound works for all depths. The obstacle was controlling test functions that cross between a finite set of tree vertices and the rest. The paper adds one correction term at the exit vertices.

The six companion papers build related examples. One proves the sharper bound D² ≥ 1 + k/4 for a closely related space. Another computes the exact moduli for a special subspace of L1, the space of integrable functions. They are max{t/2, t − 1} and max{0, t − 2}.

How sure is this? Lean, a computer proof checker, has formal statements of the main results of all seven papers. For the main space, Lean covers reflexivity, the midpoint bound, the absence of AUC norms and the diamond bound. Some auxiliary deductions in the diamond paper assume companion inputs. So the papers prove their main results, as far as the Lean statements match. No specialist review is published.

What it doesn't show. This does not change the known positive results for reflexive spaces with unconditional asymptotic structure; the example lies outside that class.

Where it came from

This result comes from an unreleased OpenAI model, published in the openai/math release on 6 October 2026.

Hype check

Lean-checked answers to a recorded problem in a specialist area. The main space is a single constructed example; its numerical constants depend on the given norm. No specialist review is published.

Words used

Banach space
A space of vectors with a length rule, called a norm, in which limits of sequences behave well.
Reflexive
A Banach space that equals its double dual, the space of linear measurements of its linear measurements.
Equivalent norm
A new length rule that differs from the old one by at most fixed factors.
AUC
Asymptotically uniformly convex: x + ty gains length whenever y points far enough out.
AMUC
Asymptotically midpoint uniformly convex: the average length of x + ty and x − ty gains when y points far out.
Countably branching diamond
A graph built by repeatedly replacing each edge with countably many two-edge paths.
Distortion
The most an embedding stretches some distances relative to others, as a ratio.

The original papers (7)

All quoted lines (9)

  1. Dilworth, Kutzarova, Randrianarivony, Revalski, and Zhivkov introduced AMUC and constructed an AMUC norm on ℓ2\ell_2 which is not AUC.
    Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space Back to text
  2. Their question about equivalence up to renorming has already been answered negatively by Baudier, using the Kadets–Werner modification of the Bourgain–Rosenthal space.
    Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space Back to text
  3. Baudier, Causey, Dilworth, Kutzarova, Randrianarivony, Schlumprecht, and Zhang proved that an equivalent AMUC norm prevents uniform embeddings of the countably branching diamonds.
    Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space Back to text
  4. This gives a negative answer to the reflexive diamond question recorded as Problem 39 in. The metric obstruction is unchanged by equivalent renorming, although the numerical constants refer to the specified dual norm.
    Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space Back to text
  5. We construct a separable reflexive real Banach space whose given norm is asymptotically midpoint uniformly convex but which admits no asymptotically uniformly convex equivalent norm.
    Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space Back to text
  6. Its averaged midpoint modulus is at least 1+t2/12−1\sqrt{1+t^2/12}-1, and the countably branching diamond of depth kk has distortion at least 1+k/12\sqrt{1+k/12} in this space.
    Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space Back to text
  7. We add one correction at their first exit vertices.
    Asymptotic midpoint uniform convexity and unbounded diamond distortion in a reflexive tree space Back to text
  8. In the dual of a finite-height segment forest, every distortion-DD embedding of the depth-kk diamond satisfies D2≥1+k/4D^2\ge1+k/4. The same bound holds in the infinite-height coordinate predual.
    Distortion of countably branching diamonds from midpoint and tree energies Back to text
  9. For an infinite-dimensional real subspace of L1L^1 whose unit ball is totally bounded in measure and whose norm has the Daugavet property, we compute the averaged midpoint and one-sided asymptotic moduli at every unit center. They are max⁡{t/2,t−1}\max\{t/2,t-1\} and max⁡{0,t−2}\max\{0,t-2\}, respectively.
    Exact asymptotic moduli in a Daugavet subspace of L1 Back to text

Other sources

  1. openai/math, Lean scope note for family 331 The paper constructs a Banach space whose midpoint geometry is asymptotically uniformly convex even though no equivalent norm is asymptotically uniformly convex in the usual one-sided sense.
  2. openai/math release notes (2026): model provenance and review status manuscripts and supporting proof artifacts produced by an internal OpenAI model

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