FDRS_en

EVEMISSLAB Logic Matrix · EveMissLab / 一言諾科技有限公司

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[CHT] 本矩陣內所有論文之公式與數據為「啟發式模擬參數」,用於驗證理論架構與推演因果鏈,未經實證校準,請勿作為現實物理測量數據引用 or 處理。EVEMISSLAB 採行「邏輯先行(Logic-First)」原則:概念架構與系統因果映射優先於統計實證,但不排除未來實證對接。


[ENG] The numerical parameters within these frameworks are illustrative model coefficients used for structural verification and causal mapping; they are not empirically calibrated and must not be treated as physical measurements. This matrix operates on a Logic-First principle: conceptual architecture and causal mapping take precedence over statistical empiricism, without precluding future empirical reconciliation.

[原始碼檢視 / source view]

-- FDRS.lean (English Annotated Version)
-- Lean 4 Formalization of Flattened Dimensional Reconstructive Theory II
-- EveMissLab Series: EML-FDRS-2026-LEAN-v1.0
-- Author: Neo.K (EveMissLab)
-- Date: June 2026
-- Companion paper: EML-FDRS-2026-v2.1
--   "Flattened Dimensional Reconstructive Theory II:
--    A Unified Framework of Connection Operators"

-- Import Lean 4's core List module for dimension and singular value list operations.
import Init.Data.List

-- =================================================================
-- Part I: Helper Function Definitions
-- =================================================================

-- Computes the sum of a list of natural numbers.
def sumNat : List Nat → Nat
  | [] => 0
  | x :: xs => x + sumNat xs

-- Computes the sum of squares of a list of rationals.
-- Represents the total singular value energy of a boundary operator.
def sumSq : List Rat → Rat
  | [] => 0
  | x :: xs => x * x + sumSq xs

-- Computes the sum of a list of rationals (general accumulator).
def sumRat : List Rat → Rat
  | [] => 0
  | x :: xs => x + sumRat xs

-- Flattens a list of lists into a single list.
-- Used to aggregate all singular values across all boundary operators.
def flatten {α : Type} : List (List α) → List α
  | [] => []
  | x :: xs => x ++ flatten xs

-- =================================================================
-- Part II: Core Structure — FDRS Admissible Chain Complex
-- =================================================================

/--
### Definition 2.4: FDRS Admissible Chain Complex (`FDRSComplex`)

An algebraic representation of a finite-dimensional FDRS-admissible chain complex
C_*(H) = (C_n → C_{n-1} → ... → C_0).

Rather than encoding the full geometric structure, this formalization
represents the complex via:
- `dim`: the sequence of chain group dimensions (dim[k] = dim C_k)
- `sing`: the sequence of singular value lists for each boundary operator
  (sing[i] = singular values of ∂_{i+1} : C_{i+1} → C_i)

Five admissibility constraints ensure the complex lies in Ch_FDRS(ℝ)
as defined in the companion paper.
-/
structure FDRSComplex where
  -- dim[k] = dimension of chain group C_k.
  dim : List Nat

  -- sing[i] = list of singular values of the boundary operator ∂_{i+1} : C_{i+1} → C_i.
  -- sing.length = dim.length - 1 (one boundary operator per adjacent pair of chain groups).
  sing : List (List Rat)

  -- Admissibility Condition 1: The complex has at least one chain group.
  dim_pos : dim.length >= 1

  -- Admissibility Condition 2: The number of boundary operators is exactly dim.length - 1.
  sing_len : sing.length + 1 = dim.length

  -- Admissibility Condition 3: The number of singular values of ∂_{i+1} equals dim C_{i+1}.
  -- Formally: |sing[i]| = dim[i+1] for all valid i.
  sing_shapes : ∀ i, (h : i < sing.length) → (sing.get ⟨i, h⟩).length = dim.get ⟨i + 1, by
    have h1 : i + 1 < sing.length + 1 := Nat.add_lt_add_right h 1
    rw [sing_len] at h1
    exact h1
  ⟩

  -- Admissibility Condition 4: Injectivity of each boundary operator.
  -- rank(∂_{i+1}) = dim C_{i+1} is encoded as dim C_{i+1} ≤ dim C_i,
  -- together with Condition 5 (all singular values positive), which implies
  -- full column rank (injectivity): rank = dim C_{i+1}.
  injective_cond : ∀ i, (h : i < sing.length) → dim.get ⟨i + 1, by
    have h1 : i + 1 < sing.length + 1 := Nat.add_lt_add_right h 1
    rw [sing_len] at h1
    exact h1
  ⟩ <= dim.get ⟨i, by
    have h1 : i < sing.length + 1 := Nat.lt_trans h (Nat.lt_succ_self _)
    rw [sing_len] at h1
    exact h1
  ⟩

  -- Admissibility Condition 5: All singular values are strictly positive.
  -- Together with Condition 4, this guarantees injectivity (no hidden degree collapse).
  sing_pos : ∀ i, (h : i < sing.length) → ∀ val ∈ sing.get ⟨i, h⟩, val > 0

-- =================================================================
-- Part III: Connection Operators and Dimension Reduction (Δ)
-- =================================================================

/--
### Definition 3.5: Single-step dimension reduction Δ₁ — dimension evolution.

Eliminates the top-dimensional chain group C_n and folds it into the
cokernel of ∂_n at C_{n-1}: the new dimension at level n-1 becomes
dim C_{n-1} - dim C_n (= dim(coker ∂_n) under admissibility).
-/
def delta1Dim (dim : List Nat) : List Nat :=
  match dim.reverse with
  | [] => []
  | [_] => []
  | cn :: cn1 :: rest => ((cn1 - cn) :: rest).reverse

-- Single-step dimension reduction Δ₁ — singular value evolution.
-- Discards the singular values of the top boundary operator ∂_n,
-- reflecting the information absorbed into the cokernel construction.
def delta1Sing (sing : List (List Rat)) : List (List Rat) :=
  match sing.reverse with
  | [] => []
  | _ :: rest => rest.reverse

/-- k-step dimension reduction Δ_k — iterated application of Δ₁. -/
-- Dimension evolution over k steps (iterate delta1Dim k times).
def deltaKDim (dim : List Nat) (k : Nat) : List Nat :=
  match k with
  | 0 => dim
  | k' + 1 => delta1Dim (deltaKDim dim k')

-- Singular value evolution over k steps (iterate delta1Sing k times).
def deltaKSing (sing : List (List Rat)) (k : Nat) : List (List Rat) :=
  match k with
  | 0 => sing
  | k' + 1 => delta1Sing (deltaKSing sing k')

-- =================================================================
-- Part IV: Information Distortion Operator D
-- =================================================================

/--
### Definition 3.1: Single-step information distortion operator D(Δ₁).

Defined as the ratio of the singular value energy of the discarded top
boundary operator ∂_n to the total dimension of the original complex.
This measures the fraction of total information energy lost in one step.
-/
def distortion1 (H : FDRSComplex) : Rat :=
  if h : H.sing.length > 0 then
    let n := H.sing.length - 1
    have hn : n < H.sing.length := Nat.sub_lt h (by decide)
    let top_sing := H.sing.get ⟨n, hn⟩
    sumSq top_sing / (sumNat H.dim : Rat)
  else
    0

-- Computes the total discarded singular value energy over k reduction steps
-- (the energy of the top k boundary operators, taken from the end of sing).
def sumTopKSq (sing : List (List Rat)) (k : Nat) : Rat :=
  sumSq (flatten (sing.drop (sing.length - k)))

-- Cumulative distortion D(Δ_k): total discarded energy normalized by total dimension.
def distortionK (H : FDRSComplex) (k : Nat) : Rat :=
  sumTopKSq H.sing k / (sumNat H.dim : Rat)

-- =================================================================
-- Part V: Auxiliary Lemmas
-- =================================================================

-- Rational arithmetic: additive distribution of division over addition.
-- (a + b) / c = a / c + b / c
theorem Rat.add_div (a b c : Rat) : (a + b) / c = a / c + b / c := by
  rw [Rat.div_def, Rat.div_def, Rat.div_def]
  exact Rat.add_mul a b c⁻¹

-- Rational arithmetic: left distributivity of multiplication over subtraction.
-- a * (b - c) = a * b - a * c
theorem Rat.mul_sub (a b c : Rat) : a * (b - c) = a * b - a * c := by
  rw [Rat.sub_eq_add_neg, Rat.mul_add, Rat.mul_neg, ← Rat.sub_eq_add_neg]

-- List Lemma 1: flatten distributes over list concatenation.
-- flatten (l₁ ++ l₂) = flatten l₁ ++ flatten l₂
theorem flatten_append {α : Type} (l₁ l₂ : List (List α)) :
    flatten (l₁ ++ l₂) = flatten l₁ ++ flatten l₂ := by
  induction l₁ with
  | nil => simp [flatten]
  | cons h t ih =>
    simp [flatten, List.append_assoc, ih]

-- List Lemma 2: sumSq is additive over list concatenation.
-- sumSq (l₁ ++ l₂) = sumSq l₁ + sumSq l₂
theorem sumSq_append (l₁ l₂ : List Rat) :
    sumSq (l₁ ++ l₂) = sumSq l₁ + sumSq l₂ := by
  induction l₁ with
  | nil =>
    simp [sumSq]
    rw [Rat.zero_add]
  | cons h t ih =>
    simp only [List.cons_append, sumSq, ih]
    rw [Rat.add_assoc]

-- List Lemma 3: Dropping (n + 1) elements equals dropping n then taking the tail.
-- l.drop (n + 1) = (l.drop n).tail
-- (Proved directly from Init without requiring Mathlib's List.drop_succ.)
theorem drop_succ {α : Type} (n : Nat) (l : List α) : l.drop (n + 1) = (l.drop n).tail := by
  induction l generalizing n with
  | nil => simp [List.drop, List.tail]
  | cons x xs ih =>
    cases n with
    | zero => simp [List.drop, List.tail]
    | succ n' =>
      simp only [List.drop]
      rw [ih n']

-- List Lemma 4: delta1Sing is equivalent to reversing, removing the head, then reversing back.
-- This connects the pattern-match definition to standard list tail operations.
theorem delta1Sing_spec (sing : List (List Rat)) :
    delta1Sing sing = (sing.reverse.tail).reverse := by
  unfold delta1Sing
  cases h : sing.reverse with
  | nil => simp
  | cons x xs => simp

-- List Lemma 5: k-step singular value reduction deltaKSing drops the last k entries.
-- deltaKSing sing k = (sing.reverse.drop k).reverse
theorem deltaKSing_spec (sing : List (List Rat)) (k : Nat) :
    deltaKSing sing k = (sing.reverse.drop k).reverse := by
  induction k with
  | zero => simp [deltaKSing, List.drop]
  | succ n ih =>
    simp only [deltaKSing, delta1Sing_spec, ih]
    rw [List.reverse_reverse]
    rw [drop_succ]

-- List Lemma 6 (Bridge Lemma): k-step reduction equals List.take (length - k).
-- deltaKSing sing k = sing.take (sing.length - k)
--
-- Key insight: the proof uses List.drop_reverse from Init.Data.List,
-- which states: l.reverse.drop k = (l.take (l.length - k)).reverse
-- This single Init lemma bridges deltaKSing to List.take in three rewrites,
-- avoiding any Mathlib dependency.
theorem deltaKSing_eq_take (sing : List (List Rat)) (k : Nat) :
    deltaKSing sing k = sing.take (sing.length - k) := by
  rw [deltaKSing_spec]       -- Rewrite LHS to (sing.reverse.drop k).reverse
  rw [List.drop_reverse]     -- Apply Init's drop_reverse: (l.reverse.drop k) = (l.take (len-k)).reverse
  rw [List.reverse_reverse]  -- Cancel double reversal, yielding sing.take (sing.length - k)

-- Core Energy Lemma: Energy partition.
-- The retained singular value energy plus the discarded singular value energy
-- equals the total singular value energy of the original complex.
-- sumSq(flatten(Δ_k(H))) + sumTopKSq(H, k) = sumSq(flatten(H))
theorem energy_partition (sing : List (List Rat)) (k : Nat) :
    sumSq (flatten (deltaKSing sing k)) + sumTopKSq sing k = sumSq (flatten sing) := by
  unfold sumTopKSq
  rw [deltaKSing_eq_take]
  -- Establish that sing splits into its take and drop components.
  have h_split : sing.take (sing.length - k) ++ sing.drop (sing.length - k) = sing :=
    List.take_append_drop (sing.length - k) sing
  -- Lift the split to the flatten level.
  have h_flat : flatten (sing.take (sing.length - k) ++ sing.drop (sing.length - k)) = flatten sing := by
    rw [h_split]
  rw [flatten_append] at h_flat
  -- Lift the split to the sumSq level.
  have h_sum : sumSq (flatten (sing.take (sing.length - k)) ++ flatten (sing.drop (sing.length - k))) = sumSq (flatten sing) := by
    rw [h_flat]
  rw [sumSq_append] at h_sum
  exact h_sum

-- =================================================================
-- Part VI: Main Theorems
-- =================================================================

/--
### Theorem 5.1 (Non-trivial version): Energy Conservation

The cumulative distortion D(Δ_k) of a k-step dimension reduction, plus the
normalized retained energy, equals the total normalized energy of the original complex:

  distortionK(H, k) + sumSq(flatten(Δ_k(H))) / dim(H) = sumSq(flatten(H)) / dim(H)

This is the substantive mathematical content of Theorem 5.1 in EML-FDRS-2026-v2.1:
the discarded singular value energy precisely accounts for the information distortion,
with no hidden residual error terms.

In particular, this theorem establishes that D(Δ_k) is not merely defined to equal
the SVD energy ratio (which would be a tautology), but that this ratio is verified
to correctly represent the energy lost under iterative application of the Δ_k operator.
-/
theorem energy_conservation (H : FDRSComplex) (k : Nat) :
    distortionK H k + (sumSq (flatten (deltaKSing H.sing k)) / (sumNat H.dim : Rat)) =
    sumSq (flatten H.sing) / (sumNat H.dim : Rat) := by
  unfold distortionK
  rw [← Rat.add_div]    -- Combine the two fractions over the common denominator.
  rw [Rat.add_comm]     -- Reorder: retained energy + discarded energy.
  rw [energy_partition] -- Apply energy_partition: the sum equals total energy. QED.

/-- Corollary: The original definitional identity remains valid.
    distortionK is defined as sumTopKSq / dim by construction (rfl). -/
theorem D_delta_spectral_correspondence (H : FDRSComplex) (k : Nat) :
  distortionK H k = sumTopKSq H.sing k / (sumNat H.dim : Rat) := by
  rfl

/--
### Definition: Spectral-mode distortion operator D_spec(Δ₁).

In spectral observation mode (mode = spec), the distortion of a single-step
reduction is measured as the energy of the top boundary operator's singular values
divided by the *total singular value energy* across all boundary operators
(rather than the total chain group dimension as in linear mode).

This asymmetry between D_lin and D_spec is the algebraic source of the
R-Δ non-commutativity (Theorem 5.2).
-/
def distortionSpec1 (H : FDRSComplex) : Rat :=
  if h : H.sing.length > 0 then
    let n := H.sing.length - 1
    have hn : n < H.sing.length := Nat.sub_lt h (by decide)
    let top_sing := H.sing.get ⟨n, hn⟩
    sumSq top_sing / sumSq (flatten H.sing)
  else
    0

/--
### Theorem 5.2: R-Δ Non-commutativity Theorem

Let:
  σ_top²    = singular value energy of the top boundary operator ∂_n
  E_total   = total singular value energy (sum over all boundary operators)
  D_total   = total chain group dimension

Then the non-commutativity measure between the representation transformation
operator R (mode switching) and the dimension meta-operator Δ (reduction) satisfies:

  D_spec(Δ₁(H)) - D_lin(Δ₁(H)) = σ_top² · (1/E_total - 1/D_total)

This precisely quantifies the order-dependence: applying R before Δ (switch mode
then reduce) produces a different distortion reading than applying Δ before R
(reduce then switch mode). The non-commutative gap is determined entirely by the
spectral data of the boundary operators.
-/
theorem R_delta_non_commutativity (H : FDRSComplex) (h : H.sing.length > 0) :
  let D_spec_delta := distortionSpec1 H
  let D_lin_delta := distortion1 H
  let energy_top := sumSq (H.sing.get ⟨H.sing.length - 1, Nat.sub_lt h (by decide)⟩)
  let total_energy := sumSq (flatten H.sing)
  let total_dim := sumNat H.dim
  D_spec_delta - D_lin_delta = energy_top * (1 / total_energy - 1 / (total_dim : Rat)) := by
  -- Introduce the let-bound local definitions.
  intro D_spec_delta D_lin_delta energy_top total_energy total_dim
  -- Unfold all local definitions to expose their underlying expressions.
  unfold D_spec_delta D_lin_delta energy_top total_energy total_dim
  -- Unfold the distortion operator definitions.
  unfold distortionSpec1 distortion1
  -- Reduce the if-then-else branches using h (sing.length > 0).
  simp only [h, ↓reduceDIte]
  -- Rewrite all rational division as multiplication by inverse (x / y = x * y⁻¹).
  rw [Rat.div_def, Rat.div_def, Rat.div_def (1 : Rat), Rat.div_def (1 : Rat)]
  -- Simplify the multiplicative identity: 1 * x⁻¹ = x⁻¹.
  rw [Rat.one_mul, Rat.one_mul]
  -- Apply left distributivity: A * B⁻¹ - A * C⁻¹ = A * (B⁻¹ - C⁻¹).
  -- After this rewrite, both sides are syntactically identical, closing the goal.
  rw [Rat.mul_sub]

-- =================================================================
-- Compilation Status: EML-FDRS-2026-LEAN-v1.0
-- Lean 4 compiler exit code: 0
-- Errors: 0 | Warnings: 0 | Sorry: 0
-- Dependencies: Init.Data.List only (Mathlib-free)
-- =================================================================
原始檔(供 RAG/下載):papers/FDRS_en.lean [lean]