linear_neumann_off_diagonal_contribution_small_with_lambda
ProvedRole. 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. Off-diagonal part of Lemma 4.5: after decoupling, Bernstein control of the auxiliary matrix and the fixed-matrix sampling theorem make the off-diagonal first Neumann correction small with high probability.
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 3 subclaims: linear Neumann off diagonal decoupled contribution small with lambda; linear Neumann off diagonal decoupling transfer; sample ratio between zero and one.
import Definitions.Def_matrix_completion_neumann open MatrixCompletion
theorem linear_neumann_off_diagonal_contribution_small_with_lambda :
∃ Coff coff : ℝ, 0 < Coff ∧ 0 < coff ∧
∀ (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 * μ₁ * max (Real.sqrt μ₀) μ₁ *
(↑(max n₁ n₂)) * (r : ℝ) *
(β * Real.log (↑(max n₁ n₂))) →
bernoulliEventProb ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
(fun Omega =>
spectralNorm
(linearNeumannOffDiagonalContribution Omega S
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) ≤
Coff * Real.rpow lam (-1)) ≥
1 - coff * Real.rpow (↑(max n₁ n₂)) (-β) := by
sorry