Result 088, Convex and metric geometry

Sharp projection-body inequalities and a counterexample to simplex maximization

Proves Petty's projection-volume conjecture in the remaining dimensions n ≥ 4: ellipsoids uniquely minimize projection-body volume at fixed body volume. Also establishes the full Lutwak–Petty projection inequalities. In contrast, products of simplices exceed Brannen's proposed simplex maximum for normalized projection-body volume by an exponential factor in every sufficiently large dimension.

Lean formalization Proof

The bigger picture

Why it matters

How small or large can a convex body's collection of shadows be when the body itself has fixed volume? The manuscripts report a sharp minimum and show that a proposed maximum fails.

What changes?

A projection body is a new convex shape encoding shadow volumes: its supporting-plane distance from the origin in each direction equals the original body's projection volume on the perpendicular hyperplane. The first manuscript claims that, in every dimension at least four, ellipsoids alone minimize projection-body volume among convex bodies of fixed volume. The second reports that a product of two ten-dimensional simplices beats a twenty-dimensional simplex in normalized projection-body volume, a comparison independent of scale.

What does that help mathematicians do?

The claimed minimum supplies both a sharp lower bound and an equality criterion: attaining it forces the original body to be an ellipsoid. On the maximum side, the supplied summary reports that products of simplices exceed the proposed simplex bound by an exponential factor in every sufficiently large dimension. Thus the failure is not marginal, and a correct upper-extremum theory must accommodate these product shapes.

Are there practical applications?

The immediate value is foundational: these claims clarify how a body's volume constrains the volume of a shape assembled from its directional shadows. They identify the reported minimizing geometry while ruling out simplices as universal maximizers. The counterexamples guide further searches for upper bounds, but do not identify the true maximizers.

This section was generated by GPT-6 Astra Medium. This explanation is based on the result summary and manuscript abstracts below. This context is separate from OpenAI's source text.

2 manuscripts

Petty’s projection-volume conjecture in dimensions at least four

September 24, 2026 30 pages Main result formalized in Lean

We prove that ellipsoids uniquely minimize the volume of the projection body among convex bodies of fixed volume in every dimension at least four. This proves Petty's projection-volume conjecture in these dimensions.

Cite (BibTeX)
@misc{OAI:Pettys-projection-volume-conjecture-in-dimensions-at-least-four-September-24-2026,
  author = {{OpenAI}},
  title = {{Petty's projection-volume conjecture in dimensions at least four}},
  howpublished = {OpenAI Math Release preprint
                  \href{https://github.com/openai/math/blob/main/preprints/Pettys-projection-volume-conjecture-in-dimensions-at-least-four-September-24-2026/paper.pdf}{OAI:Pettys-projection-volume-conjecture-in-dimensions-at-least-four-September-24-2026}},
  year = {2026}
}

A product counterexample to the simplex maximum for projection-body volume

September 24, 2026 7 pages Main result formalized in Lean

The product of two ten-dimensional simplices has larger normalized projection-body volume than a twenty-dimensional simplex. This gives a counterexample to Brannen's proposed simplex maximum.

Cite (BibTeX)
@misc{OAI:A-product-counterexample-to-the-simplex-maximum-for-projection-body-volume-September-24-2026,
  author = {{OpenAI}},
  title = {{A product counterexample to the simplex maximum for projection-body volume}},
  howpublished = {OpenAI Math Release preprint
                  \href{https://github.com/openai/math/blob/main/preprints/A-product-counterexample-to-the-simplex-maximum-for-projection-body-volume-September-24-2026/paper.pdf}{OAI:A-product-counterexample-to-the-simplex-maximum-for-projection-body-volume-September-24-2026}},
  year = {2026}
}

Lean formalization

OpenAI's note on what the formalization covers, from lean/docs/088.md.

Sharp projection-body inequalities and a counterexample to simplex maximization

The following describes the scope of the Lean formalization related to the following accompanying paper(s):

Scope

Petty's projection-volume conjecture predicts

∣ΠK∣∣K∣n−1≥κn−1nκn2−n,\displaystyle \frac{|\Pi K|}{|K|^{n-1}}\ge \kappa_{n-1}^{n}\kappa_n^{2-n},

with equality exactly for ellipsoids; here κj\kappa_j is the volume of the Euclidean unit ball in dimension jj. The formalization establishes this for every convex body K⊂RnK\subset\mathbb R^n and every n≥4n\ge4.

Other inequalities in the projection-body family are not included.

Brannen's simplex-maximization conjecture predicts that a simplex maximizes normalized projection-body volume ∣ΠK∣/∣K∣n−1|\Pi K|/|K|^{n-1} in dimension nn. The formalized counterexample is the product of two ten-dimensional simplices. Its normalized projection volume, divided by that of a twenty-dimensional simplex, is

22,355,47622,020,096>1.\displaystyle \frac{22{,}355{,}476}{22{,}020{,}096}>1.

Thus the proposed maximum fails in dimension 2020. The paper's exponential-factor result for every sufficiently large dimension is not included.

Comparator links

Result Comparator statement
Petty's projection-volume inequality PettyProjectionVolume.lean
Failure of the simplex upper bound in dimension 20 ProjectionCounterexample.lean
Explicit product-of-simplices counterexample ProjectionVolume.lean

Data from github.com/openai/math at commit adc7f12, committed October 6, 2026 at 21:58 UTC, last checked for changes about 9 hours ago. Titles, subjects, summaries, abstracts and Lean notes are OpenAI's; page counts are read from the PDFs. The map, related results, search, kinds of results and the named-problem index are Emergent Mind's, built with text embeddings and an LLM, and may contain errors.

An Emergent Mind Labs project. Emergent Mind is not affiliated with OpenAI. None of these results has been peer reviewed. Cite the manuscripts themselves, using the BibTeX on each result's page.