quadratic_neumann_first_index_distinct_centered_decoupled_threshold_from_centered_sampling_bound
DisprovedRole. It belongs to the golfing/Neumann-series certificate branch, where the certificate is decomposed into linear and quadratic sampling terms.
Problem and notation. Exact matrix completion asks when an unknown low-rank real matrix can be recovered from a random subset of its entries. Here has rank , entries are observed, and . Recovery means nuclear-norm minimization: minimize among matrices agreeing with on the observed entries. Probability notation. is the fixed-cardinality success probability: is chosen uniformly among all subsets of entries with , and the event is that the convex program uniquely returns . In Bernoulli nodes, or means each entry is sampled independently with probability , usually . Coherence notation. The object records SVD/singular-vector data for . The hypotheses and are the Candes-Recht incoherence assumptions: measures how spread out the singular vector spaces are, and measures the largest entry of the sign matrix . The parameter controls polynomial failure probabilities such as . For certificate nodes, is the tangent space at , and are the tangent and normal projections, and keeps only observed entries. The Neumann-series estimates control the dual certificate used to prove uniqueness of nuclear-norm recovery.
Claim. Deterministic threshold absorption for the decoupled centered contribution. For fixed Ω₂, the coefficient entry bound and the centered sampling spectral event in Ω₁ imply the desired spectral threshold after absorbing the scalar prefactor.
Lecture-note formulation:
The constants in this node are universal existential constants; the theorem asserts that some positive constants with these roles exist.
Decomposition status. A corresponding proof sketch reduces this node to smaller mathematical subclaims. The checked reduction uses 1 subclaim: prefactored centered sampling fluctuation quadratic threshold from entry decay.
import Definitions.Def_matrix_completion_neumann open MatrixCompletion
theorem quadratic_neumann_first_index_distinct_centered_decoupled_threshold_from_centered_sampling_bound
(Cfixed : ℝ) :
0 < Cfixed →
∃ Cthreshold : ℝ, 0 < Cthreshold ∧
∀ (β lam : ℝ), 2 < β → 1 ≤ lam →
∀ (n₁ n₂ r m : ℕ) (M : Matrix (Fin n₁) (Fin n₂) ℝ)
(μ₀ μ₁ : ℝ) (S : SVD M r),
0 < n₁ → 0 < n₂ → 0 < r → m ≤ n₁ * n₂ →
1 ≤ μ₀ → 1 ≤ μ₁ →
A0 S μ₀ → A1 S μ₁ →
(m : ℝ) ≥
lam * Real.rpow μ₀ ((4 : ℝ) / 3) *
(↑(max n₁ n₂)) * Real.rpow (r : ℝ) ((4 : ℝ) / 3) *
(β * Real.log (↑(max n₁ n₂))) →
∀ Ccoef : ℝ, 0 < Ccoef →
∀ Omega1 Omega2 : Finset (Fin n₁ × Fin n₂),
QuadraticFirstIndexDistinctCenteredCoefficientBound Omega2 S
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
(Ccoef * Real.rpow lam (-1)) →
quadraticNeumannFirstIndexDistinctCenteredDecoupledContribution
Omega1 Omega2 S ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) =
((((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))⁻¹) *
(1 - 2 * ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))))) •
centeredSamplingFluctuation Omega1
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
(quadraticFirstIndexDistinctCenteredCoefficientMatrix Omega2 S
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) →
CenteredSamplingSpectralBound Omega1
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
(quadraticFirstIndexDistinctCenteredCoefficientMatrix Omega2 S
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))))
(Cfixed * Real.sqrt
((β * (↑(max n₁ n₂)) *
Real.log (↑(max n₁ n₂))) /
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) *
entrySupNorm
(quadraticFirstIndexDistinctCenteredCoefficientMatrix Omega2 S
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))))) →
spectralNorm
(quadraticNeumannFirstIndexDistinctCenteredDecoupledContribution
Omega1 Omega2 S
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) ≤
(Cthreshold * Ccoef) * Real.rpow lam (-((3 : ℝ) / 2)) := by
sorry