-- File: optlib/Optlib/Algorithm/SubgradientMethod.lean
Theorem statement:
theorem extracted_formal_statement_102.{u_1} {E : Type u_1} [inst : NormedAddCommGroup E] [inst_1 : InnerProductSpace ℝ E]
  [inst_2 : CompleteSpace E] {f : E → ℝ} (xm x₀ : E) {alg : subgradient_method f x₀} {s : ℝ}
  (hm : IsMinOn f Set.univ xm) (ha' : ∀ (n : ℕ), subgradient_method.a f x₀ n * ‖subgradient_method.g f x₀ n‖ = s)
  (hs : s > 0) (k : ℕ)
  (heq :
    (Set.range fun x => f (subgradient_method.x f x₀ ↑x)) =
      {x | ∃ i ∈ Finset.range (k + 1), f (subgradient_method.x f x₀ i) = x})
  (hnek : Nonempty { x // x ∈ Finset.range (k + 1) })
  (h' : ∀ ⦃x g : E⦄, g ∈ SubderivAt f x → ‖g‖ ≤ ↑(subgradient_method.G f x₀)) ⦃i : ℕ⦄ (left : i ≥ 0) (hi₂ : i ≤ k)
  (this_1 :
    ‖subgradient_method.x f x₀ i - xm‖ ^ 2 -
          2 * subgradient_method.a f x₀ i *
            (sInf {x | ∃ i ∈ Finset.range (k + 1), f (subgradient_method.x f x₀ i) = x} - f xm) +
        ‖subgradient_method.a f x₀ i‖ ^ 2 * ‖subgradient_method.g f x₀ i‖ ^ 2 =
      ‖subgradient_method.x f x₀ i - xm‖ ^ 2 +
          -(2 * subgradient_method.a f x₀ i *
              (sInf {x | ∃ i ∈ Finset.range (k + 1), f (subgradient_method.x f x₀ i) = x} - f xm)) +
        ‖subgradient_method.a f x₀ i‖ ^ 2 * ‖subgradient_method.g f x₀ i‖ ^ 2)
  (hxm : f xm ≥ f (subgradient_method.x f x₀ i) + ⟪subgradient_method.g f x₀ i, xm - subgradient_method.x f x₀ i⟫_ℝ)
  (this : f (subgradient_method.x f x₀ i) ∈ Set.range fun x => f (subgradient_method.x f x₀ ↑x)) :
  BddBelow (Set.range fun x => f (subgradient_method.x f x₀ ↑x)) := sorry

Tactic state before:
E : Type u_1
inst✝² : NormedAddCommGroup E
inst✝¹ : InnerProductSpace ℝ E
inst✝ : CompleteSpace E
f : E → ℝ
xm x₀ : E
alg : subgradient_method f x₀
s : ℝ
hm : IsMinOn f Set.univ xm
ha' : ∀ (n : ℕ), subgradient_method.a f x₀ n * ‖subgradient_method.g f x₀ n‖ = s
hs : s > 0
k : ℕ
heq :
  (Set.range fun x => f (subgradient_method.x f x₀ ↑x)) =
    {x | ∃ i ∈ Finset.range (k + 1), f (subgradient_method.x f x₀ i) = x}
hnek : Nonempty { x // x ∈ Finset.range (k + 1) }
h' : ∀ ⦃x g : E⦄, g ∈ SubderivAt f x → ‖g‖ ≤ ↑(subgradient_method.G f x₀)
i : ℕ
left✝ : i ≥ 0
hi₂ : i ≤ k
this✝ :
  ‖subgradient_method.x f x₀ i - xm‖ ^ 2 -
        2 * subgradient_method.a f x₀ i *
          (sInf {x | ∃ i ∈ Finset.range (k + 1), f (subgradient_method.x f x₀ i) = x} - f xm) +
      ‖subgradient_method.a f x₀ i‖ ^ 2 * ‖subgradient_method.g f x₀ i‖ ^ 2 =
    ‖subgradient_method.x f x₀ i - xm‖ ^ 2 +
        -(2 * subgradient_method.a f x₀ i *
            (sInf {x | ∃ i ∈ Finset.range (k + 1), f (subgradient_method.x f x₀ i) = x} - f xm)) +
      ‖subgradient_method.a f x₀ i‖ ^ 2 * ‖subgradient_method.g f x₀ i‖ ^ 2
hxm : f xm ≥ f (subgradient_method.x f x₀ i) + ⟪subgradient_method.g f x₀ i, xm - subgradient_method.x f x₀ i⟫_ℝ
this : f (subgradient_method.x f x₀ i) ∈ Set.range fun x => f (subgradient_method.x f x₀ ↑x)
⊢ BddBelow (Set.range fun x => f (subgradient_method.x f x₀ ↑x))

Tactic: apply Finite.bddBelow_range

Tactic state after:

Premises used:
Finite.bddBelow_range : ∀ {α : Type u_1} {β : Type u_2} [inst : Finite β] [inst : Nonempty α] [inst : Preorder α]
  [inst_3 : IsDirected α fun x1 x2 => x1 ≥ x2] (f : β → α), BddBelow (Set.range f)