Exact-image bounds for independent affine columns
Lax342547.AffineImages · concepts/Lax342547/AffineImages.lean · lax-342547
No public endorsements yet.
Loading review…
Sign in with ORCIDNatural Language Statement
Lemma
Every column may have its own affine translation space, provided they all contain one common subspace S. An injective nominal coefficient matrix of rank t then has at least t dim(S) uniform image bits. The estimate is valid for every t, with no bounded-rank assumption.
Concept map
Evidence
Lean source view on GitHub
| 1 | import Lax342547.FiniteLinearLaw |
| 2 | |
| 3 | /-! |
| 4 | --- |
| 5 | title: Exact-image bounds for independent affine columns |
| 6 | type: lemma |
| 7 | --- |
| 8 | Every column may have its own affine translation space, provided they |
| 9 | all contain one common subspace S. An injective nominal coefficient |
| 10 | matrix of rank t then has at least t dim(S) uniform image bits. The |
| 11 | estimate is valid for every t, with no bounded-rank assumption. |
| 12 | -/ |
| 13 | |
| 14 | namespace Lax342547.AffineImages |
| 15 | |
| 16 | open Lax342547.MomentSpace |
| 17 | open scoped ENNReal |
| 18 | |
| 19 | variable {J T V : Type} [Fintype J] [Fintype T] |
| 20 | [AddCommGroup V] [Module Binary V] |
| 21 | |
| 22 | def evaluation (S : J → Submodule Binary V) (C : Matrix J T Binary) : |
| 23 | (∀ j, S j) →ₗ[Binary] (T → V) where |
| 24 | toFun x t := ∑ j, C j t • (x j).val |
| 25 | map_add' x y := by ext t; simp [smul_add, Finset.sum_add_distrib] |
| 26 | map_smul' c x := by ext t; simp [Finset.smul_sum, smul_smul, mul_comm] |
| 27 | |
| 28 | def columnMap (v : J → V) : (J → Binary) →ₗ[Binary] V where |
| 29 | toFun c := ∑ j, c j • v j |
| 30 | map_add' c d := by simp [add_smul, Finset.sum_add_distrib] |
| 31 | map_smul' s c := by simp [Finset.smul_sum, smul_smul] |
| 32 | |
| 33 | noncomputable def law [DecidableEq J] (S : J → Submodule Binary V) [∀ j, Fintype (S j)] |
| 34 | (C : Matrix J T Binary) (a : J → V) : PMF (T → V) := |
| 35 | (PMF.uniformOfFintype (∀ j, S j)).map |
| 36 | (fun x => (fun t => ∑ j, C j t • a j) + evaluation S C x) |
| 37 | |
| 38 | axiom evaluation_rank [FiniteDimensional Binary V] |
| 39 | (S : J → Submodule Binary V) (S₀ : Submodule Binary V) |
| 40 | (hS : ∀ j, S₀ ≤ S j) (C : Matrix J T Binary) (hC : Function.Injective C.mulVec) : |
| 41 | Fintype.card T * Module.finrank Binary S₀ ≤ |
| 42 | Module.finrank Binary (LinearMap.range (evaluation S C)) |
| 43 | |
| 44 | axiom image_bound [DecidableEq J] [FiniteDimensional Binary V] |
| 45 | (S : J → Submodule Binary V) [∀ j, Fintype (S j)] |
| 46 | (S₀ : Submodule Binary V) (hS : ∀ j, S₀ ≤ S j) |
| 47 | (C : Matrix J T Binary) (hC : Function.Injective C.mulVec) (a : J → V) (y : T → V) : |
| 48 | law S C a y ≤ 1 / (2 : ℝ≥0∞) ^ (Fintype.card T * Module.finrank Binary S₀) |
| 49 | |
| 50 | axiom dependence_bound [DecidableEq J] [FiniteDimensional Binary V] |
| 51 | (S : J → Submodule Binary V) [∀ j, Fintype (S j)] |
| 52 | (S₀ : Submodule Binary V) (hS : ∀ j, S₀ ≤ S j) (a : J → V) : |
| 53 | (PMF.uniformOfFintype (∀ j, S j)).toOuterMeasure |
| 54 | {x | ¬ Function.Injective (columnMap (fun j => a j + (x j).val))} ≤ |
| 55 | (2 : ℝ≥0∞) ^ Fintype.card J / 2 ^ Module.finrank Binary S₀ |
| 56 | |
| 57 | axiom extension_dependence_bound {U : Type} [AddCommGroup U] [Module Binary U] [Fintype U] |
| 58 | [DecidableEq J] [FiniteDimensional Binary V] |
| 59 | (P : U →ₗ[Binary] V) (hP : Function.Injective P) |
| 60 | (S : J → Submodule Binary V) [∀ j, Fintype (S j)] |
| 61 | (S₀ : Submodule Binary V) (hS : ∀ j, S₀ ≤ S j) (a : J → V) : |
| 62 | (PMF.uniformOfFintype (∀ j, S j)).toOuterMeasure |
| 63 | {x | ¬ Function.Injective (fun z : U × (J → Binary) => |
| 64 | P z.1 + columnMap (fun j => a j + (x j).val) z.2)} ≤ |
| 65 | (Fintype.card U : ℝ≥0∞) * 2 ^ Fintype.card J / 2 ^ Module.finrank Binary S₀ |
| 66 | |
| 67 | end Lax342547.AffineImages |
| 68 |
Discussion
Ask a question or add context. Endorsements and structured flags are kept in the review panel above.
0 comments