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Problem 02 Milestone — Compressed strict convex equality reduces dimension two

Proved
RybinAI2026.P02.compressed_strict_convex_equality_reduces_dimension_two

by wenxinzhang · Sep 4, 2026 · Mathlib c5ea003 (Lean v4.30.0)

convexityhilbert-spacesoperator-theorypositive-contractions

For indices i,c∈{0,1}i,c\in\{0,1\}i,c∈{0,1}, identify Fin⁡(2)→R\operatorname{Fin}(2)\to\mathbb RFin(2)→R with R2\mathbb R^2R2 and set Q=[0,1]2Q=[0,1]^2Q=[0,1]2. Let SSS consist of a 2×22\times22×2 complex matrix UUU satisfying U∗U=IU^*U=IU∗U=I and UU∗=IUU^*=IUU∗=I, together with points λ0,λ1∈Q\lambda_0,\lambda_1\in Qλ0​,λ1​∈Q. Define Ac=Udiag⁡(λ0(c),λ1(c))U∗A_c=U\operatorname{diag}(\lambda_0(c),\lambda_1(c))U^*Ac​=Udiag(λ0​(c),λ1​(c))U∗ and ΦS(f)=Udiag⁡(f(λ0),f(λ1))U∗\Phi_S(f)=U\operatorname{diag}(f(\lambda_0),f(\lambda_1))U^*ΦS​(f)=Udiag(f(λ0​),f(λ1​))U∗. Let PPP be a 2×22\times22×2 complex matrix satisfying P∗=PP^*=PP∗=P and P2=PP^2=PP2=P. Assume a compression witness is supplied: it consists of another matrix VVV satisfying V∗V=IV^*V=IV∗V=I and VV∗=IVV^*=IVV∗=I, points μ0,μ1∈Q\mu_0,\mu_1\in Qμ0​,μ1​∈Q, and matrices Bc=Vdiag⁡(μ0(c),μ1(c))V∗B_c=V\operatorname{diag}(\mu_0(c),\mu_1(c))V^*Bc​=Vdiag(μ0​(c),μ1​(c))V∗ satisfying B0=PA0PB_0=PA_0PB0​=PA0​P and B1=PA1PB_1=PA_1PB1​=PA1​P. For an arbitrary function f:R2→Rf:\mathbb R^2\to\mathbb Rf:R2→R, assume that fff is continuous relative to QQQ, and that QQQ is convex and, for every pair of distinct x,y∈Qx,y\in Qx,y∈Q and every a,b>0a,b>0a,b>0 with a+b=1a+b=1a+b=1, one has f(ax+by)<af(x)+bf(y)f(ax+by)<a f(x)+b f(y)f(ax+by)<af(x)+bf(y). Define ΦC(f)=Vdiag⁡(f(μ0),f(μ1))V∗\Phi_C(f)=V\operatorname{diag}(f(\mu_0),f(\mu_1))V^*ΦC​(f)=Vdiag(f(μ0​),f(μ1​))V∗, and assume PΦS(f)P=PΦC(f)PP\Phi_S(f)P=P\Phi_C(f)PPΦS​(f)P=PΦC​(f)P. Then PPP commutes separately with both original coordinate operators: PA0=A0PPA_0=A_0PPA0​=A0​P and PA1=A1PPA_1=A_1PPA1​=A1​P. The assumptions permit P=0P=0P=0 and P=IP=IP=I, permit repeated spectral points and points on the boundary of QQQ, impose no rank or properness condition on PPP, and constrain fff only on QQQ; moreover, the compression witness is assumed as data rather than asserted to exist.

Preamble
import Definitions.Def_rybin2026_p02_compressed_convex_calculus

open Matrix Set
Formal statement
namespace RybinAI2026.P02

/-- The equality-rigidity statement for two commuting positive contractions in dimension two. -/
theorem compressed_strict_convex_equality_reduces_dimension_two
    (S : JointSpectralData 2)
    (P : Matrix (Fin 2) (Fin 2) ℂ) (hP : IsOrthogonalProjection P)
    (C : CompressionWitness S P)
    (f : (Fin 2 → ℝ) → ℝ) (hf_cont : ContinuousOn f unitSquare)
    (hf_strict : StrictConvexOn ℝ unitSquare f)
    (heq : P * S.functional f * P = P * C.compressed.functional f * P) :
    Reduces P (S.operator 0) ∧ Reduces P (S.operator 1) := by
  sorry

end RybinAI2026.P02
Source
https://rybindmitry.github.io/problems/2.html
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What the Lean code literally says, in plain math · gpt-5.6-sol

For indices i,c∈{0,1}i,c\in\{0,1\}i,c∈{0,1}, identify Fin⁡(2)→R\operatorname{Fin}(2)\to\mathbb RFin(2)→R with R2\mathbb R^2R2 and set Q=[0,1]2Q=[0,1]^2Q=[0,1]2. Let SSS consist of a 2×22\times22×2 complex matrix UUU satisfying U∗U=IU^*U=IU∗U=I and UU∗=IUU^*=IUU∗=I, together with points λ0,λ1∈Q\lambda_0,\lambda_1\in Qλ0​,λ1​∈Q. Define Ac=Udiag⁡(λ0(c),λ1(c))U∗A_c=U\operatorname{diag}(\lambda_0(c),\lambda_1(c))U^*Ac​=Udiag(λ0​(c),λ1​(c))U∗ and ΦS(f)=Udiag⁡(f(λ0),f(λ1))U∗\Phi_S(f)=U\operatorname{diag}(f(\lambda_0),f(\lambda_1))U^*ΦS​(f)=Udiag(f(λ0​),f(λ1​))U∗. Let PPP be a 2×22\times22×2 complex matrix satisfying P∗=PP^*=PP∗=P and P2=PP^2=PP2=P. Assume a compression witness is supplied: it consists of another matrix VVV satisfying V∗V=IV^*V=IV∗V=I and VV∗=IVV^*=IVV∗=I, points μ0,μ1∈Q\mu_0,\mu_1\in Qμ0​,μ1​∈Q, and matrices Bc=Vdiag⁡(μ0(c),μ1(c))V∗B_c=V\operatorname{diag}(\mu_0(c),\mu_1(c))V^*Bc​=Vdiag(μ0​(c),μ1​(c))V∗ satisfying B0=PA0PB_0=PA_0PB0​=PA0​P and B1=PA1PB_1=PA_1PB1​=PA1​P. For an arbitrary function f:R2→Rf:\mathbb R^2\to\mathbb Rf:R2→R, assume that fff is continuous relative to QQQ, and that QQQ is convex and, for every pair of distinct x,y∈Qx,y\in Qx,y∈Q and every a,b>0a,b>0a,b>0 with a+b=1a+b=1a+b=1, one has f(ax+by)<af(x)+bf(y)f(ax+by)<a f(x)+b f(y)f(ax+by)<af(x)+bf(y). Define ΦC(f)=Vdiag⁡(f(μ0),f(μ1))V∗\Phi_C(f)=V\operatorname{diag}(f(\mu_0),f(\mu_1))V^*ΦC​(f)=Vdiag(f(μ0​),f(μ1​))V∗, and assume PΦS(f)P=PΦC(f)PP\Phi_S(f)P=P\Phi_C(f)PPΦS​(f)P=PΦC​(f)P. Then PPP commutes separately with both original coordinate operators: PA0=A0PPA_0=A_0PPA0​=A0​P and PA1=A1PPA_1=A_1PPA1​=A1​P. The assumptions permit P=0P=0P=0 and P=IP=IP=I, permit repeated spectral points and points on the boundary of QQQ, impose no rank or properness condition on PPP, and constrain fff only on QQQ; moreover, the compression witness is assumed as data rather than asserted to exist.

Human review
  • Endorsed by Shuze Chen · Sep 5, 2026

  • Endorsed by wenxinzhang · Sep 5, 2026

    Confirmed by the mission captain (proposal self-audit).

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