l3lab/ntp-mathlib huggingface dataset:
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
hδ : ∀ (n : ℕ), δ < δ + 1 / (↑n + 1)
h : ∀ (n : ℕ), ∃ i, ‖u - ↑i‖ < δ + 1 / (↑n + 1)
⊢ ∃ w, ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)

Next tactic:
let w : ℕ → K := fun n => Classical.choose (h n)
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
hδ : ∀ (n : ℕ), δ < δ + 1 / (↑n + 1)
h : ∀ (n : ℕ), ∃ i, ‖u - ↑i‖ < δ + 1 / (↑n + 1)
w : ℕ → ↑K := fun n => Classical.choose (_ : ∃ i, ‖u - ↑i‖ < δ + 1 / (↑n + 1))
⊢ ∃ w, ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)

Next tactic:
exact ⟨w, fun n => Classical.choose_spec (h n)⟩
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
exists_seq : ∃ w, ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
⊢ ∃ v ∈ K, ‖u - v‖ = ⨅ w, ‖u - ↑w‖

Next tactic:
rcases exists_seq with ⟨w, hw⟩
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  rcases exists_seq with ⟨w, hw⟩
  

Proof state:
case intro
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
w : ℕ → ↑K
hw : ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
⊢ ∃ v ∈ K, ‖u - v‖ = ⨅ w, ‖u - ↑w‖

Next tactic:
have norm_tendsto : Tendsto (fun n => ‖u - w n‖) atTop (nhds δ) := by
    have h : Tendsto (fun _ : ℕ => δ) atTop (nhds δ) := tendsto_const_nhds
    have h' : Tendsto (fun n : ℕ => δ + 1 / (n + 1)) atTop (nhds δ) := by
      convert h.add tendsto_one_div_add_atTop_nhds_0_nat
      simp only [add_zero]
    exact tendsto_of_tendsto_of_tendsto_of_le_of_le h h' (fun x => δ_le _) fun x => le_of_lt (hw _)
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  rcases exists_seq with ⟨w, hw⟩
  have norm_tendsto : Tendsto (fun n => ‖u - w n‖) atTop (nhds δ) := by
    

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
w : ℕ → ↑K
hw : ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
⊢ Tendsto (fun n => ‖u - ↑(w n)‖) atTop (𝓝 δ)

Next tactic:
have h : Tendsto (fun _ : ℕ => δ) atTop (nhds δ) := tendsto_const_nhds
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  rcases exists_seq with ⟨w, hw⟩
  have norm_tendsto : Tendsto (fun n => ‖u - w n‖) atTop (nhds δ) := by
    have h : Tendsto (fun _ : ℕ => δ) atTop (nhds δ) := tendsto_const_nhds
    

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
w : ℕ → ↑K
hw : ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
h : Tendsto (fun x => δ) atTop (𝓝 δ)
⊢ Tendsto (fun n => ‖u - ↑(w n)‖) atTop (𝓝 δ)

Next tactic:
have h' : Tendsto (fun n : ℕ => δ + 1 / (n + 1)) atTop (nhds δ) := by
      convert h.add tendsto_one_div_add_atTop_nhds_0_nat
      simp only [add_zero]
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  rcases exists_seq with ⟨w, hw⟩
  have norm_tendsto : Tendsto (fun n => ‖u - w n‖) atTop (nhds δ) := by
    have h : Tendsto (fun _ : ℕ => δ) atTop (nhds δ) := tendsto_const_nhds
    have h' : Tendsto (fun n : ℕ => δ + 1 / (n + 1)) atTop (nhds δ) := by
      

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
w : ℕ → ↑K
hw : ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
h : Tendsto (fun x => δ) atTop (𝓝 δ)
⊢ Tendsto (fun n => δ + 1 / (↑n + 1)) atTop (𝓝 δ)

Next tactic:
convert h.add tendsto_one_div_add_atTop_nhds_0_nat
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  rcases exists_seq with ⟨w, hw⟩
  have norm_tendsto : Tendsto (fun n => ‖u - w n‖) atTop (nhds δ) := by
    have h : Tendsto (fun _ : ℕ => δ) atTop (nhds δ) := tendsto_const_nhds
    have h' : Tendsto (fun n : ℕ => δ + 1 / (n + 1)) atTop (nhds δ) := by
      convert h.add tendsto_one_div_add_atTop_nhds_0_nat
      

Proof state:
case h.e'_5.h.e'_3
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
w : ℕ → ↑K
hw : ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
h : Tendsto (fun x => δ) atTop (𝓝 δ)
⊢ δ = δ + 0

Next tactic:
simp only [add_zero]
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  rcases exists_seq with ⟨w, hw⟩
  have norm_tendsto : Tendsto (fun n => ‖u - w n‖) atTop (nhds δ) := by
    have h : Tendsto (fun _ : ℕ => δ) atTop (nhds δ) := tendsto_const_nhds
    have h' : Tendsto (fun n : ℕ => δ + 1 / (n + 1)) atTop (nhds δ) := by
      convert h.add tendsto_one_div_add_atTop_nhds_0_nat
      simp only [add_zero]
    

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
w : ℕ → ↑K
hw : ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
h : Tendsto (fun x => δ) atTop (𝓝 δ)
h' : Tendsto (fun n => δ + 1 / (↑n + 1)) atTop (𝓝 δ)
⊢ Tendsto (fun n => ‖u - ↑(w n)‖) atTop (𝓝 δ)

Next tactic:
exact tendsto_of_tendsto_of_tendsto_of_le_of_le h h' (fun x => δ_le _) fun x => le_of_lt (hw _)
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  rcases exists_seq with ⟨w, hw⟩
  have norm_tendsto : Tendsto (fun n => ‖u - w n‖) atTop (nhds δ) := by
    have h : Tendsto (fun _ : ℕ => δ) atTop (nhds δ) := tendsto_const_nhds
    have h' : Tendsto (fun n : ℕ => δ + 1 / (n + 1)) atTop (nhds δ) := by
      convert h.add tendsto_one_div_add_atTop_nhds_0_nat
      simp only [add_zero]
    exact tendsto_of_tendsto_of_tendsto_of_le_of_le h h' (fun x => δ_le _) fun x => le_of_lt (hw _)
  -- Step 2: Prove that the sequence `w : ℕ → K` is a Cauchy sequence
  have seq_is_cauchy : CauchySeq fun n => (w n : F) := by
    

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
w : ℕ → ↑K
hw : ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
norm_tendsto : Tendsto (fun n => ‖u - ↑(w n)‖) atTop (𝓝 δ)
⊢ CauchySeq fun n => ↑(w n)

Next tactic:
rw [cauchySeq_iff_le_tendsto_0]
/-- Existence of minimizers
Let `u` be a point in a real inner product space, and let `K` be a nonempty complete convex subset.
Then there exists a (unique) `v` in `K` that minimizes the distance `‖u - v‖` to `u`.
 -/
theorem exists_norm_eq_iInf_of_complete_convex {K : Set F} (ne : K.Nonempty) (h₁ : IsComplete K)
    (h₂ : Convex ℝ K) : ∀ u : F, ∃ v ∈ K, ‖u - v‖ = ⨅ w : K, ‖u - w‖ := fun u => by
  let δ := ⨅ w : K, ‖u - w‖
  letI : Nonempty K := ne.to_subtype
  have zero_le_δ : 0 ≤ δ := le_ciInf fun _ => norm_nonneg _
  have δ_le : ∀ w : K, δ ≤ ‖u - w‖ := ciInf_le ⟨0, Set.forall_range_iff.2 fun _ => norm_nonneg _⟩
  have δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖ := fun w hw => δ_le ⟨w, hw⟩
  -- Step 1: since `δ` is the infimum, can find a sequence `w : ℕ → K` in `K`
  -- such that `‖u - w n‖ < δ + 1 / (n + 1)` (which implies `‖u - w n‖ --> δ`);
  -- maybe this should be a separate lemma
  have exists_seq : ∃ w : ℕ → K, ∀ n, ‖u - w n‖ < δ + 1 / (n + 1) := by
    have hδ : ∀ n : ℕ, δ < δ + 1 / (n + 1) := fun n =>
      lt_add_of_le_of_pos le_rfl Nat.one_div_pos_of_nat
    have h := fun n => exists_lt_of_ciInf_lt (hδ n)
    let w : ℕ → K := fun n => Classical.choose (h n)
    exact ⟨w, fun n => Classical.choose_spec (h n)⟩
  rcases exists_seq with ⟨w, hw⟩
  have norm_tendsto : Tendsto (fun n => ‖u - w n‖) atTop (nhds δ) := by
    have h : Tendsto (fun _ : ℕ => δ) atTop (nhds δ) := tendsto_const_nhds
    have h' : Tendsto (fun n : ℕ => δ + 1 / (n + 1)) atTop (nhds δ) := by
      convert h.add tendsto_one_div_add_atTop_nhds_0_nat
      simp only [add_zero]
    exact tendsto_of_tendsto_of_tendsto_of_le_of_le h h' (fun x => δ_le _) fun x => le_of_lt (hw _)
  -- Step 2: Prove that the sequence `w : ℕ → K` is a Cauchy sequence
  have seq_is_cauchy : CauchySeq fun n => (w n : F) := by
    rw [cauchySeq_iff_le_tendsto_0]
    -- splits into three goals
    

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
inst✝⁴ : IsROrC 𝕜
inst✝³ : NormedAddCommGroup E
inst✝² : NormedAddCommGroup F
inst✝¹ : InnerProductSpace 𝕜 E
inst✝ : InnerProductSpace ℝ F
K : Set F
ne : Set.Nonempty K
h₁ : IsComplete K
h₂ : Convex ℝ K
u : F
δ : ℝ := ⨅ w, ‖u - ↑w‖
this : Nonempty ↑K := Set.Nonempty.to_subtype ne
zero_le_δ : 0 ≤ δ
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
w : ℕ → ↑K
hw : ∀ (n : ℕ), ‖u - ↑(w n)‖ < δ + 1 / (↑n + 1)
norm_tendsto : Tendsto (fun n => ‖u - ↑(w n)‖) atTop (𝓝 δ)
⊢ ∃ b, (∀ (n : ℕ), 0 ≤ b n) ∧ (∀ (n m N : ℕ), N ≤ n → N ≤ m → dist ↑(w n) ↑(w m) ≤ b N) ∧ Tendsto b atTop (𝓝 0)

Next tactic:
let b := fun n : ℕ => 8 * δ * (1 / (n + 1)) + 4 * (1 / (n + 1)) * (1 / (n + 1))