centered_sampling_fluctuation_linear_neumann_prefactor_bound_from_entry_scale
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. Raw deterministic step for a linear Neumann centered-sampling fluctuation: the fixed-matrix sampling event and the entry-scale hypothesis give the unabsorbed prefactor scale.
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. This node is currently a leaf problem in the decomposition tree, intended to be proved directly by later agents.
import Definitions.Def_matrix_completion_tangent open MatrixCompletion
theorem centered_sampling_fluctuation_linear_neumann_prefactor_bound_from_entry_scale
(Cfixed : ℝ) :
0 < Cfixed →
∃ Cpref : ℝ, 0 < Cpref ∧
∀ (β : ℝ), 2 < β →
∀ (n₁ n₂ r m : ℕ) (μ₀ μ₁ : ℝ),
0 < n₁ → 0 < n₂ → 0 < r → m ≤ n₁ * n₂ →
1 ≤ μ₀ → 1 ≤ μ₁ →
let p : ℝ := (m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))
∀ Centry : ℝ, 0 < Centry →
let entryScale : ℝ :=
Centry * μ₁ *
Real.sqrt ((r : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) *
Real.sqrt
((μ₀ * (↑(max n₁ n₂)) * (r : ℝ) *
(β * Real.log (↑(max n₁ n₂)))) / (m : ℝ))
∀ (Omega : Finset (Fin n₁ × Fin n₂))
(X Y : Matrix (Fin n₁) (Fin n₂) ℝ),
Y = centeredSamplingFluctuation Omega p X →
entrySupNorm X ≤ entryScale →
CenteredSamplingSpectralBound Omega
p X
(Cfixed * Real.sqrt
((β * (↑(max n₁ n₂)) *
Real.log (↑(max n₁ n₂))) /
p) *
entrySupNorm X) →
spectralNorm Y ≤
Cpref * entryScale *
Real.sqrt
((β * (↑(max n₁ n₂)) *
Real.log (↑(max n₁ n₂))) /
p) := by
sorry