Explicit idealization input from the cuspidal cubic
OpenMathoverflow507128.idealizationInput_explicitcommutative-algebrainvertible-modulespicard-groups
There exist explicit commutative-ring, module, and ideal data satisfying the three inputs needed by the idealization argument: a proper invertible ideal, injective tensor multiplication, and a module element killed by each nonunit.
Preamble
import Mathlib.Algebra.TrivSqZeroExt.Basic import Mathlib.LinearAlgebra.TensorProduct.Prod import Mathlib.RingTheory.Localization.FractionRing import Mathlib.RingTheory.PicardGroup import Definitions.Def_RybinP18_CuspidalCubicInput import Definitions.Def_RybinP18_MO507128 /-! # MathOverflow 507128: the idealization step This file deliberately does not import or clone `formal-conjectures`. It copies the statement of the target theorem and proves the square-zero idealization argument using mathlib. The input for the idealization is constructed explicitly from the cuspidal cubic `Y² = X³`. Thus the final `#print axioms` contains no problem-specific axiom. -/
Formal statement
namespace Mathoverflow507128 universe u v variable (D : Type u) [CommRing D] variable (M : Type v) [AddCommGroup M] [Module D M] local instance p2m_Theorems_Thm_Mathoverflow507128_idealizationInput_explicit_1 : Module Dᵐᵒᵖ M := Module.compHom M ((RingHom.id D).fromOpposite mul_comm) local instance p2m_Theorems_Thm_Mathoverflow507128_idealizationInput_explicit_2 : IsCentralScalar D M := ⟨fun _ _ => rfl⟩ local notation "R" => TrivSqZeroExt D M /-- The explicit cuspidal-cubic input: * `D = ℂ[X,Y]/(Y²-X³) ≅ ℂ[t²,t³]`; * `P = (X-1,Y-1)`; * `M = ⨁ (r : ℂ), (r ≠ 1) → ℂ_r`, where `D` acts by evaluation `(X,Y) = (r²,r³)`. The normalization argument in `CuspidalCubicInput` proves that every nonunit is annihilated in some component with `r ≠ 1`. -/ theorem idealizationInput_explicit : IdealizationInput := by sorry end Mathoverflow507128 namespace Mathoverflow507128 end Mathoverflow507128
Source
CUHK-Shenzhen AI Math Problem 18, https://rybindmitry.github.io/problems/18.html. Lean formalization by Patricia Purtill and Kenta Kitamura, discussed at https://github.com/google-deepmind/formal-conjectures/pull/4644#issuecomment-5089566133; staged from Kenta Kitamura's Apache-2.0 repository https://github.com/KitaKen1/mo507128-lean at commit e9507429c01c4288089e4af1c92a03b7d1e17f74.