openAI-math-dataset / lean /OAI /Analysis /Graph /Gradients.lean
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Add lean source and binary files (part 18)
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import Mathlib
namespace OAI
/-! Closed gradient subspaces of connected oriented multigraphs. -/
open scoped lp
noncomputable section
namespace SingerFour
structure OrientedGraph (V E : Type*) where
tail : E → V
head : E → V
namespace OrientedGraph
variable {V E : Type*} (G : OrientedGraph V E)
inductive Walk : V → V → Type _
| nil (v : V) : Walk v v
| forward {v : V} (e : E) (p : Walk v (G.tail e)) : Walk v (G.head e)
| backward {v : V} (e : E) (p : Walk v (G.head e)) : Walk v (G.tail e)
def Connected : Prop := ∀ v w : V, Nonempty (G.Walk v w)
def gradients : Submodule ℝ (ℓ²(E, ℝ)) where
carrier := {c | ∃ f : V → ℝ, ∀ e, c e = f (G.head e) - f (G.tail e)}
zero_mem' := ⟨0, by intro e; simp⟩
add_mem' := by
rintro c d ⟨f, hf⟩ ⟨g, hg⟩
refine ⟨f + g, ?_⟩
intro e
change c e + d e = (f (G.head e) + g (G.head e)) -
(f (G.tail e) + g (G.tail e))
rw [hf e, hg e]
ring
smul_mem' := by
rintro r c ⟨f, hf⟩
refine ⟨r • f, ?_⟩
intro e
change r * c e = r * f (G.head e) - r * f (G.tail e)
rw [hf e, mul_sub]
@[simp]
theorem mem_gradients (c : ℓ²(E, ℝ)) :
c ∈ G.gradients ↔ ∃ f : V → ℝ, ∀ e, c e = f (G.head e) - f (G.tail e) := Iff.rfl
def Walk.integral {G : OrientedGraph V E} {v w : V} : G.Walk v w → (ℓ²(E, ℝ) →L[ℝ] ℝ)
| .nil _ => 0
| .forward e p => p.integral + lp.evalCLM ℝ (fun _ : E => ℝ) 2 e
| .backward e p => p.integral - lp.evalCLM ℝ (fun _ : E => ℝ) 2 e
theorem Walk.integral_gradient {G : OrientedGraph V E} {c : ℓ²(E, ℝ)} {f : V → ℝ}
(hf : ∀ e, c e = f (G.head e) - f (G.tail e)) {v w : V}
(p : G.Walk v w) : p.integral c = f w - f v := by
induction p with
| nil => simp [Walk.integral]
| forward e p ih =>
change p.integral c + c e = f (G.head e) - f v
rw [ih, hf e]
ring
| backward e p ih =>
change p.integral c - c e = f (G.tail e) - f v
rw [ih, hf e]
ring
theorem eq_of_zero_differences (hG : G.Connected) {f : V → ℝ}
(hf : ∀ e, f (G.head e) = f (G.tail e)) (v w : V) : f v = f w := by
have hgrad : ∀ e, (0 : ℓ²(E, ℝ)) e = f (G.head e) - f (G.tail e) := by
intro e
simp [hf e]
have h := (Classical.choice (hG v w)).integral_gradient hgrad
simpa only [map_zero, eq_comm, sub_eq_zero] using h.symm
theorem potential_unique (hG : G.Connected) (o : V) {f g : V → ℝ}
(hedges : ∀ e, f (G.head e) - f (G.tail e) = g (G.head e) - g (G.tail e))
(ho : f o = g o) : f = g := by
funext v
have hzero : ∀ e, (f - g) (G.head e) = (f - g) (G.tail e) := by
intro e
have := hedges e
dsimp
linarith
have h := G.eq_of_zero_differences hG hzero o v
dsimp at h
linarith
theorem l2_eq_zero_of_constant [Infinite V] (f : ℓ²(V, ℝ)) {r : ℝ}
(hf : ∀ v, f v = r) : f = 0 := by
have hs : Summable (fun v : V => ‖r‖ ^ (2 : ℕ)) := by
simpa only [hf, ENNReal.toReal_ofNat, Real.rpow_two] using
(lp.memℓp f).summable (by norm_num : 0 < (2 : ENNReal).toReal)
have hr : r = 0 := by
have h := (summable_const_iff (β := V) (‖r‖ ^ (2 : ℕ))).mp hs
simpa using h
apply lp.ext
funext v
simpa only [lp.coeFn_zero, Pi.zero_apply, hr] using hf v
theorem l2_eq_zero_of_zero_differences [Infinite V] (hG : G.Connected)
(f : ℓ²(V, ℝ)) (hf : ∀ e, f (G.head e) = f (G.tail e)) : f = 0 := by
let o : V := Classical.choice (inferInstance : Nonempty V)
exact l2_eq_zero_of_constant f (fun v => G.eq_of_zero_differences hG hf v o)
noncomputable def normalizedPotential (hG : G.Connected) (o v : V) :
ℓ²(E, ℝ) →L[ℝ] ℝ :=
(Classical.choice (hG o v)).integral
theorem normalizedPotential_gradient (hG : G.Connected) (o v : V)
{c : ℓ²(E, ℝ)} {f : V → ℝ}
(hf : ∀ e, c e = f (G.head e) - f (G.tail e)) :
G.normalizedPotential hG o v c = f v - f o :=
Walk.integral_gradient hf _
theorem mem_gradients_iff_normalized (hG : G.Connected) (o : V) (c : ℓ²(E, ℝ)) :
c ∈ G.gradients ↔ ∀ e,
c e = G.normalizedPotential hG o (G.head e) c -
G.normalizedPotential hG o (G.tail e) c := by
constructor
· rintro ⟨f, hf⟩ e
rw [G.normalizedPotential_gradient hG o (G.head e) hf,
G.normalizedPotential_gradient hG o (G.tail e) hf, hf e]
ring
· intro hc
exact ⟨fun v => G.normalizedPotential hG o v c, hc⟩
theorem isClosed_gradients (hG : G.Connected) (o : V) :
IsClosed (G.gradients : Set (ℓ²(E, ℝ))) := by
have hset : (G.gradients : Set (ℓ²(E, ℝ))) =
⋂ e : E, {c : ℓ²(E, ℝ) | c e =
G.normalizedPotential hG o (G.head e) c -
G.normalizedPotential hG o (G.tail e) c} := by
ext c
simp only [SetLike.mem_coe, Set.mem_iInter, Set.mem_ofPred,
G.mem_gradients_iff_normalized hG o c]
rw [hset]
apply isClosed_iInter
intro e
exact isClosed_eq (lp.evalCLM ℝ (fun _ : E => ℝ) 2 e).continuous
((G.normalizedPotential hG o (G.head e)).continuous.sub
(G.normalizedPotential hG o (G.tail e)).continuous)
theorem completeSpace_gradients (hG : G.Connected) (o : V) :
CompleteSpace G.gradients :=
(G.isClosed_gradients hG o).completeSpace_coe
def finiteSupportGradients : Submodule ℝ (ℓ²(E, ℝ)) where
carrier := {c | ∃ f : V →₀ ℝ, ∀ e, c e = f (G.head e) - f (G.tail e)}
zero_mem' := ⟨0, by intro e; simp⟩
add_mem' := by
rintro c d ⟨f, hf⟩ ⟨g, hg⟩
refine ⟨f + g, ?_⟩
intro e
change c e + d e = (f (G.head e) + g (G.head e)) -
(f (G.tail e) + g (G.tail e))
rw [hf e, hg e]
ring
smul_mem' := by
rintro r c ⟨f, hf⟩
refine ⟨r • f, ?_⟩
intro e
change r * c e = r * f (G.head e) - r * f (G.tail e)
rw [hf e, mul_sub]
def LocallyFinite : Prop :=
∀ v : V, Set.Finite {e : E | G.tail e = v ∨ G.head e = v}
theorem hasFiniteSupport_differential (hG : G.LocallyFinite) (f : V →₀ ℝ) :
(Function.support (fun e => f (G.head e) - f (G.tail e))).Finite := by
classical
have hfinite : (⋃ v ∈ (f.support : Set V),
{e : E | G.tail e = v ∨ G.head e = v}).Finite :=
f.support.finite_toSet.biUnion (fun v _ => hG v)
apply hfinite.subset
intro e he
change f (G.head e) - f (G.tail e) ≠ 0 at he
have hside : G.head e ∈ f.support ∨ G.tail e ∈ f.support := by
by_contra h
push Not at h
simp [Finsupp.notMem_support_iff.mp h.1, Finsupp.notMem_support_iff.mp h.2] at he
rcases hside with hh | ht
· exact Set.mem_iUnion₂.mpr ⟨G.head e, hh, Or.inr rfl⟩
· exact Set.mem_iUnion₂.mpr ⟨G.tail e, ht, Or.inl rfl⟩
def finiteSupportDifferential (hG : G.LocallyFinite) :
(V →₀ ℝ) →ₗ[ℝ] ℓ²(E, ℝ) where
toFun f := ⟨fun e => f (G.head e) - f (G.tail e),
(memℓp_zero (G.hasFiniteSupport_differential hG f)).of_exponent_ge (by positivity)⟩
map_add' f g := by
apply lp.ext
funext e
change f (G.head e) + g (G.head e) - (f (G.tail e) + g (G.tail e)) =
(f (G.head e) - f (G.tail e)) + (g (G.head e) - g (G.tail e))
ring
map_smul' r f := by
apply lp.ext
funext e
exact mul_sub r (f (G.head e)) (f (G.tail e)) |>.symm
@[simp]
theorem finiteSupportDifferential_apply (hG : G.LocallyFinite) (f : V →₀ ℝ) (e : E) :
G.finiteSupportDifferential hG f e = f (G.head e) - f (G.tail e) := rfl
theorem range_finiteSupportDifferential (hG : G.LocallyFinite) :
LinearMap.range (G.finiteSupportDifferential hG) = G.finiteSupportGradients := by
ext c
constructor
· rintro ⟨f, rfl⟩
exact ⟨f, fun e => rfl⟩
· rintro ⟨f, hf⟩
refine ⟨f, ?_⟩
apply lp.ext
funext e
exact (hf e).symm
def zeroBoundaryGradients : Submodule ℝ (ℓ²(E, ℝ)) :=
G.finiteSupportGradients.topologicalClosure
theorem finiteSupportGradients_le : G.finiteSupportGradients ≤ G.gradients := by
rintro c ⟨f, hf⟩
exact ⟨f, hf⟩
theorem zeroBoundaryGradients_le (hG : G.Connected) (o : V) :
G.zeroBoundaryGradients ≤ G.gradients :=
G.finiteSupportGradients.topologicalClosure_minimal G.finiteSupportGradients_le
(G.isClosed_gradients hG o)
end OrientedGraph
end SingerFour
end
end OAI