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

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 → ℝ := fun n => 8 * δ * (1 / (↑n + 1)) + 4 * (1 / (↑n + 1)) * (1 / (↑n + 1))
⊢ ∃ 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:
use fun n => sqrt (b 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)⟩
  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
    let b := fun n : ℕ => 8 * δ * (1 / (n + 1)) + 4 * (1 / (n + 1)) * (1 / (n + 1))
    use fun n => sqrt (b n)
    

Proof state:
case h
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 → ℝ := fun n => 8 * δ * (1 / (↑n + 1)) + 4 * (1 / (↑n + 1)) * (1 / (↑n + 1))
⊢ (∀ (n : ℕ), 0 ≤ sqrt (b n)) ∧
    (∀ (n m N : ℕ), N ≤ n → N ≤ m → dist ↑(w n) ↑(w m) ≤ sqrt (b N)) ∧ Tendsto (fun n => sqrt (b n)) atTop (𝓝 0)

Next tactic:
constructor
/-- 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
    let b := fun n : ℕ => 8 * δ * (1 / (n + 1)) + 4 * (1 / (n + 1)) * (1 / (n + 1))
    use fun n => sqrt (b n)
    constructor
    -- first goal :  `∀ (n : ℕ), 0 ≤ sqrt (b n)`
    intro n
    exact sqrt_nonneg _
    

Proof state:
case h.right
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 → ℝ := fun n => 8 * δ * (1 / (↑n + 1)) + 4 * (1 / (↑n + 1)) * (1 / (↑n + 1))
⊢ (∀ (n m N : ℕ), N ≤ n → N ≤ m → dist ↑(w n) ↑(w m) ≤ sqrt (b N)) ∧ Tendsto (fun n => sqrt (b n)) atTop (𝓝 0)

Next tactic:
constructor
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := 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
h : Convex ℝ K
u v : F
hv : v ∈ K
⊢ ‖u - v‖ = ⨅ w, ‖u - ↑w‖ ↔ ∀ w ∈ K, inner (u - v) (w - v) ≤ 0

Next tactic:
letI : Nonempty K := ⟨⟨v, hv⟩⟩
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  

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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
⊢ ‖u - v‖ = ⨅ w, ‖u - ↑w‖ ↔ ∀ w ∈ K, inner (u - v) (w - v) ≤ 0

Next tactic:
constructor
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · 

Proof state:
case mp
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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
⊢ ‖u - v‖ = ⨅ w, ‖u - ↑w‖ → ∀ w ∈ K, inner (u - v) (w - v) ≤ 0

Next tactic:
intro eq w hw
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    

Proof state:
case mp
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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
⊢ inner (u - v) (w - v) ≤ 0

Next tactic:
let δ := ⨅ w : K, ‖u - w‖
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    

Proof state:
case mp
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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
⊢ inner (u - v) (w - v) ≤ 0

Next tactic:
let p := ⟪u - v, w - v⟫_ℝ
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    let p := ⟪u - v, w - v⟫_ℝ
    

Proof state:
case mp
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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
p : ℝ := inner (u - v) (w - v)
⊢ inner (u - v) (w - v) ≤ 0

Next tactic:
let q := ‖w - v‖ ^ 2
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    let p := ⟪u - v, w - v⟫_ℝ
    let q := ‖w - v‖ ^ 2
    

Proof state:
case mp
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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
p : ℝ := inner (u - v) (w - v)
q : ℝ := ‖w - v‖ ^ 2
⊢ inner (u - v) (w - v) ≤ 0

Next tactic:
have δ_le (w : K) : δ ≤ ‖u - w‖ := ciInf_le ⟨0, fun _ ⟨_, h⟩ => h ▸ norm_nonneg _⟩ _
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    let p := ⟪u - v, w - v⟫_ℝ
    let q := ‖w - v‖ ^ 2
    have δ_le (w : K) : δ ≤ ‖u - w‖ := ciInf_le ⟨0, fun _ ⟨_, h⟩ => h ▸ norm_nonneg _⟩ _
    

Proof state:
case mp
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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
p : ℝ := inner (u - v) (w - v)
q : ℝ := ‖w - v‖ ^ 2
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
⊢ inner (u - v) (w - v) ≤ 0

Next tactic:
have δ_le' (w) (hw : w ∈ K) : δ ≤ ‖u - w‖ := δ_le ⟨w, hw⟩
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    let p := ⟪u - v, w - v⟫_ℝ
    let q := ‖w - v‖ ^ 2
    have δ_le (w : K) : δ ≤ ‖u - w‖ := ciInf_le ⟨0, fun _ ⟨_, h⟩ => h ▸ norm_nonneg _⟩ _
    have δ_le' (w) (hw : w ∈ K) : δ ≤ ‖u - w‖ := δ_le ⟨w, hw⟩
    

Proof state:
case mp
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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
p : ℝ := inner (u - v) (w - v)
q : ℝ := ‖w - v‖ ^ 2
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
⊢ inner (u - v) (w - v) ≤ 0

Next tactic:
have (θ : ℝ) (hθ₁ : 0 < θ) (hθ₂ : θ ≤ 1) : 2 * p ≤ θ * q := by
      have : ‖u - v‖ ^ 2 ≤ ‖u - v‖ ^ 2 - 2 * θ * ⟪u - v, w - v⟫_ℝ + θ * θ * ‖w - v‖ ^ 2 :=
        calc ‖u - v‖ ^ 2
          _ ≤ ‖u - (θ • w + (1 - θ) • v)‖ ^ 2 := by
            simp only [sq]; apply mul_self_le_mul_self (norm_nonneg _)
            rw [eq]; apply δ_le'
            apply h hw hv
            exacts [le_of_lt hθ₁, sub_nonneg.2 hθ₂, add_sub_cancel'_right _ _]
          _ = ‖u - v - θ • (w - v)‖ ^ 2 := by
            have : u - (θ • w + (1 - θ) • v) = u - v - θ • (w - v) := by
              rw [smul_sub, sub_smul, one_smul]
              simp only [sub_eq_add_neg, add_comm, add_left_comm, add_assoc, neg_add_rev]
            rw [this]
          _ = ‖u - v‖ ^ 2 - 2 * θ * inner (u - v) (w - v) + θ * θ * ‖w - v‖ ^ 2 := by
            rw [@norm_sub_sq ℝ, inner_smul_right, norm_smul]
            simp only [sq]
            show
              ‖u - v‖ * ‖u - v‖ - 2 * (θ * inner (u - v) (w - v)) +
                absR θ * ‖w - v‖ * (absR θ * ‖w - v‖) =
              ‖u - v‖ * ‖u - v‖ - 2 * θ * inner (u - v) (w - v) + θ * θ * (‖w - v‖ * ‖w - v‖)
            rw [abs_of_pos hθ₁]; ring
      have eq₁ :
        ‖u - v‖ ^ 2 - 2 * θ * inner (u - v) (w - v) + θ * θ * ‖w - v‖ ^ 2 =
          ‖u - v‖ ^ 2 + (θ * θ * ‖w - v‖ ^ 2 - 2 * θ * inner (u - v) (w - v)) := by
        abel
      rw [eq₁, le_add_iff_nonneg_right] at this
      have eq₂ :
        θ * θ * ‖w - v‖ ^ 2 - 2 * θ * inner (u - v) (w - v) =
          θ * (θ * ‖w - v‖ ^ 2 - 2 * inner (u - v) (w - v))
      ring
      rw [eq₂] at this
      have := le_of_sub_nonneg (nonneg_of_mul_nonneg_right this hθ₁)
      exact this
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    let p := ⟪u - v, w - v⟫_ℝ
    let q := ‖w - v‖ ^ 2
    have δ_le (w : K) : δ ≤ ‖u - w‖ := ciInf_le ⟨0, fun _ ⟨_, h⟩ => h ▸ norm_nonneg _⟩ _
    have δ_le' (w) (hw : w ∈ K) : δ ≤ ‖u - w‖ := δ_le ⟨w, hw⟩
    have (θ : ℝ) (hθ₁ : 0 < θ) (hθ₂ : θ ≤ 1) : 2 * p ≤ θ * q := 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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
p : ℝ := inner (u - v) (w - v)
q : ℝ := ‖w - v‖ ^ 2
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
θ : ℝ
hθ₁ : 0 < θ
hθ₂ : θ ≤ 1
⊢ 2 * p ≤ θ * q

Next tactic:
have : ‖u - v‖ ^ 2 ≤ ‖u - v‖ ^ 2 - 2 * θ * ⟪u - v, w - v⟫_ℝ + θ * θ * ‖w - v‖ ^ 2 :=
        calc ‖u - v‖ ^ 2
          _ ≤ ‖u - (θ • w + (1 - θ) • v)‖ ^ 2 := by
            simp only [sq]; apply mul_self_le_mul_self (norm_nonneg _)
            rw [eq]; apply δ_le'
            apply h hw hv
            exacts [le_of_lt hθ₁, sub_nonneg.2 hθ₂, add_sub_cancel'_right _ _]
          _ = ‖u - v - θ • (w - v)‖ ^ 2 := by
            have : u - (θ • w + (1 - θ) • v) = u - v - θ • (w - v) := by
              rw [smul_sub, sub_smul, one_smul]
              simp only [sub_eq_add_neg, add_comm, add_left_comm, add_assoc, neg_add_rev]
            rw [this]
          _ = ‖u - v‖ ^ 2 - 2 * θ * inner (u - v) (w - v) + θ * θ * ‖w - v‖ ^ 2 := by
            rw [@norm_sub_sq ℝ, inner_smul_right, norm_smul]
            simp only [sq]
            show
              ‖u - v‖ * ‖u - v‖ - 2 * (θ * inner (u - v) (w - v)) +
                absR θ * ‖w - v‖ * (absR θ * ‖w - v‖) =
              ‖u - v‖ * ‖u - v‖ - 2 * θ * inner (u - v) (w - v) + θ * θ * (‖w - v‖ * ‖w - v‖)
            rw [abs_of_pos hθ₁]; ring
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    let p := ⟪u - v, w - v⟫_ℝ
    let q := ‖w - v‖ ^ 2
    have δ_le (w : K) : δ ≤ ‖u - w‖ := ciInf_le ⟨0, fun _ ⟨_, h⟩ => h ▸ norm_nonneg _⟩ _
    have δ_le' (w) (hw : w ∈ K) : δ ≤ ‖u - w‖ := δ_le ⟨w, hw⟩
    have (θ : ℝ) (hθ₁ : 0 < θ) (hθ₂ : θ ≤ 1) : 2 * p ≤ θ * q := by
      have : ‖u - v‖ ^ 2 ≤ ‖u - v‖ ^ 2 - 2 * θ * ⟪u - v, w - v⟫_ℝ + θ * θ * ‖w - v‖ ^ 2 :=
        calc ‖u - v‖ ^ 2
          _ ≤ ‖u - (θ • w + (1 - θ) • v)‖ ^ 2 := 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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
p : ℝ := inner (u - v) (w - v)
q : ℝ := ‖w - v‖ ^ 2
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
θ : ℝ
hθ₁ : 0 < θ
hθ₂ : θ ≤ 1
⊢ ‖u - v‖ ^ 2 ≤ ‖u - (θ • w + (1 - θ) • v)‖ ^ 2

Next tactic:
simp only [sq]
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    let p := ⟪u - v, w - v⟫_ℝ
    let q := ‖w - v‖ ^ 2
    have δ_le (w : K) : δ ≤ ‖u - w‖ := ciInf_le ⟨0, fun _ ⟨_, h⟩ => h ▸ norm_nonneg _⟩ _
    have δ_le' (w) (hw : w ∈ K) : δ ≤ ‖u - w‖ := δ_le ⟨w, hw⟩
    have (θ : ℝ) (hθ₁ : 0 < θ) (hθ₂ : θ ≤ 1) : 2 * p ≤ θ * q := by
      have : ‖u - v‖ ^ 2 ≤ ‖u - v‖ ^ 2 - 2 * θ * ⟪u - v, w - v⟫_ℝ + θ * θ * ‖w - v‖ ^ 2 :=
        calc ‖u - v‖ ^ 2
          _ ≤ ‖u - (θ • w + (1 - θ) • v)‖ ^ 2 := by
            simp only [sq]; 

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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
p : ℝ := inner (u - v) (w - v)
q : ℝ := ‖w - v‖ ^ 2
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
θ : ℝ
hθ₁ : 0 < θ
hθ₂ : θ ≤ 1
⊢ ‖u - v‖ * ‖u - v‖ ≤ ‖u - (θ • w + (1 - θ) • v)‖ * ‖u - (θ • w + (1 - θ) • v)‖

Next tactic:
apply mul_self_le_mul_self (norm_nonneg _)
/-- Characterization of minimizers for the projection on a convex set in a real inner product
space. -/
theorem norm_eq_iInf_iff_real_inner_le_zero {K : Set F} (h : Convex ℝ K) {u : F} {v : F}
    (hv : v ∈ K) : (‖u - v‖ = ⨅ w : K, ‖u - w‖) ↔ ∀ w ∈ K, ⟪u - v, w - v⟫_ℝ ≤ 0 := by
  letI : Nonempty K := ⟨⟨v, hv⟩⟩
  constructor
  · intro eq w hw
    let δ := ⨅ w : K, ‖u - w‖
    let p := ⟪u - v, w - v⟫_ℝ
    let q := ‖w - v‖ ^ 2
    have δ_le (w : K) : δ ≤ ‖u - w‖ := ciInf_le ⟨0, fun _ ⟨_, h⟩ => h ▸ norm_nonneg _⟩ _
    have δ_le' (w) (hw : w ∈ K) : δ ≤ ‖u - w‖ := δ_le ⟨w, hw⟩
    have (θ : ℝ) (hθ₁ : 0 < θ) (hθ₂ : θ ≤ 1) : 2 * p ≤ θ * q := by
      have : ‖u - v‖ ^ 2 ≤ ‖u - v‖ ^ 2 - 2 * θ * ⟪u - v, w - v⟫_ℝ + θ * θ * ‖w - v‖ ^ 2 :=
        calc ‖u - v‖ ^ 2
          _ ≤ ‖u - (θ • w + (1 - θ) • v)‖ ^ 2 := by
            simp only [sq]; apply mul_self_le_mul_self (norm_nonneg _)
            

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
h : Convex ℝ K
u v : F
hv : v ∈ K
this : Nonempty ↑K := Nonempty.intro { val := v, property := hv }
eq : ‖u - v‖ = ⨅ w, ‖u - ↑w‖
w : F
hw : w ∈ K
δ : ℝ := ⨅ w, ‖u - ↑w‖
p : ℝ := inner (u - v) (w - v)
q : ℝ := ‖w - v‖ ^ 2
δ_le : ∀ (w : ↑K), δ ≤ ‖u - ↑w‖
δ_le' : ∀ w ∈ K, δ ≤ ‖u - w‖
θ : ℝ
hθ₁ : 0 < θ
hθ₂ : θ ≤ 1
⊢ ‖u - v‖ ≤ ‖u - (θ • w + (1 - θ) • v)‖

Next tactic:
rw [eq]