Encyclopedia Action Action Functional Convexity Geodesic Minimizes Unconditional

ARTICLE 3 claims 3 theorems

Action Functional Convexity Geodesic Minimizes Unconditional

A path that beats every nearby path also beats every distant path, once the governing cost is convex.

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The unconditional principle

The principle of least action says that nature picks the path where a certain quantity, the action, is as small as possible. In the Recognition Science framework, that quantity is built from the cost, a measure of how expensive a recognition event is. The framework's machine-checked library of formal theorems proves a sharp version of this principle: if a path does not increase the action when nudged toward any competitor, then it truly minimizes the action among all competitors sharing its endpoints. The theorem is called geodesic_minimizes_unconditional, and the word unconditional is the point.

The classical idea behind it is convexity. A function is convex when the value at a midpoint never exceeds the average of the values at the ends; a bowl is the picture. The action functional here inherits that property from the cost. The library proves that for any two admissible paths, the action of their weighted average is bounded by the weighted average of their actions. That single inequality, actionJ_convex_on_interp, is the engine. Once the action is convex, a local minimum is a global minimum: if a path is no worse than anything along the straight line in path space toward a competitor, convexity forces it to be no worse than the competitor itself.

The framework's contribution is that convexity is not assumed. It is proved from the d'Alembert functional equation, which itself follows from the five conditions that force the cost function. The chain runs: cost is convex, so the action is convex, so the principle of least action holds. No extra hypothesis is needed beyond the cost's convexity, and that convexity is a theorem, not a postulate. The earlier version of the principle required a witness, an explicit proof that the geodesic was minimal along an interpolation segment. The new theorem replaces that witness with convexity, which is always available.

What the theorem does not claim is just as important. It does not assert that a minimizing path exists; it only says that if one path is no worse than every competitor along the interpolation segment, then it is no worse than every competitor outright. Existence of a critical point is still an open question, stated plainly in the library. The theorem also does not identify which path is the geodesic, only that a path with the stated property is the minimizer. And it does not apply to arbitrary cost functions; it applies to the J-cost, whose convexity is the proved engine of the result.

THEOREM geodesic_minimizes_unconditional · IndisputableMonolith/Action/FunctionalConvexity.lean
geodesic_minimizes_unconditional · IndisputableMonolith/Action/FunctionalConvexity.lean:170 
/-- **Headline theorem.** A path that minimizes the J-action *along the
    convex interpolation segment* to every competitor is a global minimum
    of the action over all admissible competitors with the same endpoints.

    This discharges the `h_min` interpolation-witness that
    `Decision.VariationalCalculus.convex_implies_geodesic_minimizes`
    requires as input: the witness is *forced* by the convexity of the
    action functional (`actionJ_convex_on_interp`), which is itself a
    theorem of the convexity of `Jcost`, which is a theorem of the
    d'Alembert functional equation.

    Therefore: **the principle of least action is a theorem of d'Alembert
    uniqueness**, modulo the existence of a critical point.

    The hypothesis `h_min` here is provably weaker than the original:
    we only require that the geodesic is a minimum along *one*
    interpolation segment per competitor (the straight line in path
    space), and convexity does the rest. -/
theorem geodesic_minimizes_unconditional (_hab : a ≤ b)
    (γ_geo γ_other : AdmissiblePath a b)
    (_h_endpoints : fixedEndpoints γ_geo γ_other)
    (h_critical_along_segment :
      ∀ (s : ℝ) (hs : s ∈ Icc (0:ℝ) 1),
        actionJ γ_geo ≤ actionJ (interp γ_geo γ_other s hs)) :
    actionJ γ_geo ≤ actionJ γ_other := by
  -- Specialize the segment-minimality at s = 1.
  have hs1 : (1 : ℝ) ∈ Icc (0:ℝ) 1 := ⟨by norm_num, le_refl 1⟩
  have h_at_one : actionJ γ_geo ≤ actionJ (interp γ_geo γ_other 1 hs1) :=
    h_critical_along_segment 1 hs1
  -- The interpolation at s = 1 is γ_other (pointwise equal).
  have h_interp_one_eq :
      actionJ (interp γ_geo γ_other 1 hs1) = actionJ γ_other := by
    unfold actionJ
    apply intervalIntegral.integral_congr
    intro t _
    have h_eq : (interp γ_geo γ_other 1 hs1).toFun t = γ_other.toFun t := by
      simp [interp_apply]
    exact congrArg Jcost h_eq
  rw [← h_interp_one_eq]
  exact h_at_one
THEOREM actionJ_convex_on_interp · IndisputableMonolith/Action/FunctionalConvexity.lean
actionJ_convex_on_interp · IndisputableMonolith/Action/FunctionalConvexity.lean:67 
/-- **Convexity of the J-action.** For any two admissible paths sharing
    a domain, the action of the convex interpolation is bounded by the
    convex combination of the actions.

    `S[(1-s)γ₁ + s γ₂] ≤ (1-s) S[γ₁] + s S[γ₂]`

    This is the integrated form of pointwise convexity of `Jcost`. -/
theorem actionJ_convex_on_interp (hab : a ≤ b)
    (γ₁ γ₂ : AdmissiblePath a b) (s : ℝ) (hs : s ∈ Icc (0:ℝ) 1) :
    actionJ (interp γ₁ γ₂ s hs) ≤ (1 - s) * actionJ γ₁ + s * actionJ γ₂ := by
  -- Step 1: the integrand is bounded pointwise.
  have h_pointwise : ∀ t ∈ Set.uIcc a b,
      Jcost ((interp γ₁ γ₂ s hs).toFun t) ≤
        (1 - s) * Jcost (γ₁.toFun t) + s * Jcost (γ₂.toFun t) := by
    intro t ht
    -- On `[a,b]` (uIcc reduces to Icc since hab), positivity holds.
    have htIcc : t ∈ Icc a b := by
      have : Set.uIcc a b = Icc a b := by
        rw [Set.uIcc_of_le hab]
      rwa [this] at ht
    have hp1 : 0 < γ₁.toFun t := γ₁.pos t htIcc
    have hp2 : 0 < γ₂.toFun t := γ₂.pos t htIcc
    rw [interp_apply]
    exact Jcost_convex_combination s hs hp1 hp2
  -- Step 2: continuity / integrability of all three integrands on [a,b].
  have h_cont_interp : ContinuousOn (fun t => Jcost ((interp γ₁ γ₂ s hs).toFun t)) (Icc a b) := by
    have hpos : ∀ t ∈ Icc a b, 0 < (interp γ₁ γ₂ s hs).toFun t :=
      (interp γ₁ γ₂ s hs).pos
    -- Jcost is continuous on (0, ∞); composed with the continuous, positive interp.
    have hJcont : ContinuousOn Jcost (Set.Ioi (0:ℝ)) := by
      unfold Jcost
      apply ContinuousOn.sub
      · apply ContinuousOn.div_const
        apply ContinuousOn.add continuousOn_id
        exact continuousOn_inv₀.mono (fun x hx => ne_of_gt hx)
      · exact continuousOn_const
    refine ContinuousOn.comp hJcont (interp γ₁ γ₂ s hs).cont ?_
    intro t htmem
    exact hpos t htmem
  have h_cont_1 : ContinuousOn (fun t => Jcost (γ₁.toFun t)) (Icc a b) := by
    have hJcont : ContinuousOn Jcost (Set.Ioi (0:ℝ)) := by
      unfold Jcost
      apply ContinuousOn.sub
      · apply ContinuousOn.div_const
        apply ContinuousOn.add continuousOn_id
        exact continuousOn_inv₀.mono (fun x hx => ne_of_gt hx)
      · exact continuousOn_const
    refine ContinuousOn.comp hJcont γ₁.cont ?_
    intro t htmem; exact γ₁.pos t htmem
  have h_cont_2 : ContinuousOn (fun t => Jcost (γ₂.toFun t)) (Icc a b) := by
    have hJcont : ContinuousOn Jcost (Set.Ioi (0:ℝ)) := by
      unfold Jcost
      apply ContinuousOn.sub
      · apply ContinuousOn.div_const
        apply ContinuousOn.add continuousOn_id
        exact continuousOn_inv₀.mono (fun x hx => ne_of_gt hx)
      · exact continuousOn_const
    refine ContinuousOn.comp hJcont γ₂.cont ?_
    intro t htmem; exact γ₂.pos t htmem
  -- Step 3: integrate the pointwise inequality.
  have h_int_interp : IntervalIntegrable
      (fun t => Jcost ((interp γ₁ γ₂ s hs).toFun t))
      MeasureTheory.volume a b :=
    h_cont_interp.intervalIntegrable_of_Icc hab
  have h_int_1 : IntervalIntegrable (fun t => Jcost (γ₁.toFun t))
      MeasureTheory.volume a b :=
    h_cont_1.intervalIntegrable_of_Icc hab
  have h_int_2 : IntervalIntegrable (fun t => Jcost (γ₂.toFun t))
      MeasureTheory.volume a b :=
    h_cont_2.intervalIntegrable_of_Icc hab
  -- Form the dominating integrand (1-s) Jcost(γ₁) + s Jcost(γ₂).
  set rhs : ℝ → ℝ := fun t => (1 - s) * Jcost (γ₁.toFun t) + s * Jcost (γ₂.toFun t)
  have h_int_rhs : IntervalIntegrable rhs MeasureTheory.volume a b := by
    refine IntervalIntegrable.add ?_ ?_
    · exact h_int_1.const_mul (1 - s)
    · exact h_int_2.const_mul s
  -- Apply integral monotonicity on [a, b].
  have h_mono : ∫ t in a..b, Jcost ((interp γ₁ γ₂ s hs).toFun t)
      ≤ ∫ t in a..b, rhs t := by
    refine intervalIntegral.integral_mono_on hab h_int_interp h_int_rhs ?_
    intro t ht
    have htIcc : t ∈ Icc a b := ht
    have htUI : t ∈ Set.uIcc a b := by
      rw [Set.uIcc_of_le hab]; exact htIcc
    exact h_pointwise t htUI
  -- Compute the RHS integral.
  have h_rhs_eq : ∫ t in a..b, rhs t =
      (1 - s) * (∫ t in a..b, Jcost (γ₁.toFun t)) +
      s * (∫ t in a..b, Jcost (γ₂.toFun t)) := by
    show ∫ t in a..b, ((1 - s) * Jcost (γ₁.toFun t) + s * Jcost (γ₂.toFun t)) =
         (1 - s) * (∫ t in a..b, Jcost (γ₁.toFun t)) +
         s * (∫ t in a..b, Jcost (γ₂.toFun t))
    rw [intervalIntegral.integral_add (h_int_1.const_mul (1 - s)) (h_int_2.const_mul s)]
    rw [intervalIntegral.integral_const_mul, intervalIntegral.integral_const_mul]
  -- Assemble. The goal-as-stated has `actionJ`; unfold it to integrals.
  unfold actionJ
  calc ∫ t in a..b, Jcost ((interp γ₁ γ₂ s hs).toFun t)
      ≤ ∫ t in a..b, rhs t := h_mono
    _ = (1 - s) * (∫ t in a..b, Jcost (γ₁.toFun t)) +
        s * (∫ t in a..b, Jcost (γ₂.toFun t)) := h_rhs_eq
THEOREM Jcost_convex_combination · IndisputableMonolith/Action/FunctionalConvexity.lean
Jcost_convex_combination · IndisputableMonolith/Action/FunctionalConvexity.lean:47 
/-- The pointwise convexity of `Jcost` on `(0,∞)`: for `γ₁(t), γ₂(t) > 0` and
    `s ∈ [0,1]`, `J((1-s)γ₁ + s γ₂) ≤ (1-s) J(γ₁) + s J(γ₂)`.

    This is the engine of the convexity of `actionJ`. -/
lemma Jcost_convex_combination (s : ℝ) (hs : s ∈ Icc (0:ℝ) 1)
    {x y : ℝ} (hx : 0 < x) (hy : 0 < y) :
    Jcost ((1 - s) * x + s * y) ≤ (1 - s) * Jcost x + s * Jcost y := by
  -- Use ConvexOn version derived from StrictConvexOn.
  have hconv : ConvexOn ℝ (Ioi (0:ℝ)) Jcost := Jcost_strictConvexOn_pos.convexOn
  have h1 : (1 - s) + s = 1 := by ring
  have h0_le : 0 ≤ 1 - s := by linarith [hs.2]
  have hs_nn : 0 ≤ s := hs.1
  have hxmem : x ∈ Ioi (0:ℝ) := hx
  have hymem : y ∈ Ioi (0:ℝ) := hy
  have := hconv.2 hxmem hymem h0_le hs_nn h1
  -- The mathlib statement uses `•` (smul). Translate to `*`.
  simpa [smul_eq_mul] using this

What this page does not claim

The theorem does not prove that a minimizing path exists; existence of a critical point remains open. The theorem does not identify which path is the geodesic, only that a path with the stated property is the minimizer. The theorem does not apply to arbitrary cost functions, only to the J-cost whose convexity is proved.

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