-- Mathlib/Analysis/SpecialFunctions/Gamma/BohrMollerup.lean
theorem tendsto_logGammaSeq (hf_conv : ConvexOn ℝ (Ioi 0) f)
    (hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y) (hx : 0 < x) :
    Tendsto (logGammaSeq x) atTop (𝓝 <| f x - f 1)

Proof state:
case succ
f : ℝ → ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
m : ℕ
x : ℝ
hx : 0 < x
hy : ↑m < x - 1
hy' : x - 1 ≤ ↑m + 1
this :
  ∀ᶠ (n : ℕ) in atTop,
    logGammaSeq (x - 1) n = logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)
hm :
  Tendsto
    ((fun x_1 => logGammaSeq x (x_1 - 1) + x * (log (↑(x_1 - 1) + 1) - log ↑(x_1 - 1)) - log (x - 1) + log (x - 1)) ∘
      fun a => a + 1)
    atTop (𝓝 (f (x - 1) - f 1 + log (x - 1)))
⊢ Tendsto (logGammaSeq x) atTop (𝓝 (f x - f 1))

Tactic:
have :
  ((fun x_1 : ℕ =>
        (fun n : ℕ =>
              logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1))
            x_1 +
          (fun b : ℕ => log (x - 1)) x_1) ∘
      fun a : ℕ => a + 1) =
    fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n) := by
  ext1 n
  dsimp only [Function.comp_apply]
  rw [sub_add_cancel, Nat.add_sub_cancel]

Result:
case succ
f : ℝ → ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
m : ℕ
x : ℝ
hx : 0 < x
hy : ↑m < x - 1
hy' : x - 1 ≤ ↑m + 1
this✝ :
  ∀ᶠ (n : ℕ) in atTop,
    logGammaSeq (x - 1) n = logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)
hm :
  Tendsto
    ((fun x_1 => logGammaSeq x (x_1 - 1) + x * (log (↑(x_1 - 1) + 1) - log ↑(x_1 - 1)) - log (x - 1) + log (x - 1)) ∘
      fun a => a + 1)
    atTop (𝓝 (f (x - 1) - f 1 + log (x - 1)))
this :
  ((fun x_1 =>
        (fun n => logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)) x_1 +
          (fun b => log (x - 1)) x_1) ∘
      fun a => a + 1) =
    fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)
⊢ Tendsto (logGammaSeq x) atTop (𝓝 (f x - f 1))

Proof state:
case succ
f : ℝ → ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
m : ℕ
x : ℝ
hx : 0 < x
hy : ↑m < x - 1
hy' : x - 1 ≤ ↑m + 1
this✝ :
  ∀ᶠ (n : ℕ) in atTop,
    logGammaSeq (x - 1) n = logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)
hm :
  Tendsto
    ((fun x_1 => logGammaSeq x (x_1 - 1) + x * (log (↑(x_1 - 1) + 1) - log ↑(x_1 - 1)) - log (x - 1) + log (x - 1)) ∘
      fun a => a + 1)
    atTop (𝓝 (f (x - 1) - f 1 + log (x - 1)))
this :
  ((fun x_1 =>
        (fun n => logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)) x_1 +
          (fun b => log (x - 1)) x_1) ∘
      fun a => a + 1) =
    fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)
⊢ Tendsto (logGammaSeq x) atTop (𝓝 (f x - f 1))

Tactic:
rw [this] at hm

Result:
case succ
f : ℝ → ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
m : ℕ
x : ℝ
hx : 0 < x
hy : ↑m < x - 1
hy' : x - 1 ≤ ↑m + 1
this✝ :
  ∀ᶠ (n : ℕ) in atTop,
    logGammaSeq (x - 1) n = logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)
hm : Tendsto (fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)) atTop (𝓝 (f (x - 1) - f 1 + log (x - 1)))
this :
  ((fun x_1 =>
        (fun n => logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)) x_1 +
          (fun b => log (x - 1)) x_1) ∘
      fun a => a + 1) =
    fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)
⊢ Tendsto (logGammaSeq x) atTop (𝓝 (f x - f 1))

Proof state:
f : ℝ → ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
m : ℕ
x : ℝ
hx : 0 < x
hy : ↑m < x - 1
hy' : x - 1 ≤ ↑m + 1
this :
  ∀ᶠ (n : ℕ) in atTop,
    logGammaSeq (x - 1) n = logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)
hm :
  Tendsto
    ((fun x_1 => logGammaSeq x (x_1 - 1) + x * (log (↑(x_1 - 1) + 1) - log ↑(x_1 - 1)) - log (x - 1) + log (x - 1)) ∘
      fun a => a + 1)
    atTop (𝓝 (f (x - 1) - f 1 + log (x - 1)))
⊢ ((fun x_1 =>
        (fun n => logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)) x_1 +
          (fun b => log (x - 1)) x_1) ∘
      fun a => a + 1) =
    fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)

Tactic:
ext1 n

Result:
case h
f : ℝ → ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
m : ℕ
x : ℝ
hx : 0 < x
hy : ↑m < x - 1
hy' : x - 1 ≤ ↑m + 1
this :
  ∀ᶠ (n : ℕ) in atTop,
    logGammaSeq (x - 1) n = logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)
hm :
  Tendsto
    ((fun x_1 => logGammaSeq x (x_1 - 1) + x * (log (↑(x_1 - 1) + 1) - log ↑(x_1 - 1)) - log (x - 1) + log (x - 1)) ∘
      fun a => a + 1)
    atTop (𝓝 (f (x - 1) - f 1 + log (x - 1)))
n : ℕ
⊢ ((fun x_1 =>
          (fun n => logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)) x_1 +
            (fun b => log (x - 1)) x_1) ∘
        fun a => a + 1)
      n =
    logGammaSeq x n + x * (log (↑n + 1) - log ↑n)

Proof state:
f : ℝ → ℝ
x : ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
hx : 0 < x
this : ∀ (m : ℕ), ↑m < x → x ≤ ↑m + 1 → Tendsto (logGammaSeq x) atTop (𝓝 (f x - f 1))
h✝ : x < 1
⊢ x - 1 ≤ 0

Tactic:
linarith

Result:
no goals

Proof state:
case refine_1.inr
f : ℝ → ℝ
x : ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
hx : 0 < x
this : ∀ (m : ℕ), ↑m < x → x ≤ ↑m + 1 → Tendsto (logGammaSeq x) atTop (𝓝 (f x - f 1))
h✝ : 1 ≤ x
⊢ ↑⌈x - 1⌉₊ < x

Tactic:
convert Nat.ceil_lt_add_one (by linarith : 0 ≤ x - 1)

Result:
case h.e'_4
f : ℝ → ℝ
x : ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
hx : 0 < x
this : ∀ (m : ℕ), ↑m < x → x ≤ ↑m + 1 → Tendsto (logGammaSeq x) atTop (𝓝 (f x - f 1))
h✝ : 1 ≤ x
⊢ x = x - 1 + 1

Proof state:
case h.e'_5.h.e'_3
f : ℝ → ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
m : ℕ
x : ℝ
hx : 0 < x
hy : ↑m < x - 1
hy' : x - 1 ≤ ↑m + 1
this✝¹ :
  ∀ᶠ (n : ℕ) in atTop,
    logGammaSeq (x - 1) n = logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)
hm : Tendsto (fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)) atTop (𝓝 (f (x - 1) - f 1 + log (x - 1)))
this✝ :
  ((fun x_1 =>
        (fun n => logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)) x_1 +
          (fun b => log (x - 1)) x_1) ∘
      fun a => a + 1) =
    fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)
this : f x = f (x - 1) + log (x - 1)
⊢ f x - f 1 = f (x - 1) - f 1 + log (x - 1) - x * 0

Tactic:
rw [this]

Result:
case h.e'_5.h.e'_3
f : ℝ → ℝ
hf_conv : ConvexOn ℝ (Ioi 0) f
hf_feq : ∀ {y : ℝ}, 0 < y → f (y + 1) = f y + log y
m : ℕ
x : ℝ
hx : 0 < x
hy : ↑m < x - 1
hy' : x - 1 ≤ ↑m + 1
this✝¹ :
  ∀ᶠ (n : ℕ) in atTop,
    logGammaSeq (x - 1) n = logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)
hm : Tendsto (fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)) atTop (𝓝 (f (x - 1) - f 1 + log (x - 1)))
this✝ :
  ((fun x_1 =>
        (fun n => logGammaSeq x (n - 1) + x * (log (↑(n - 1) + 1) - log ↑(n - 1)) - log (x - 1)) x_1 +
          (fun b => log (x - 1)) x_1) ∘
      fun a => a + 1) =
    fun n => logGammaSeq x n + x * (log (↑n + 1) - log ↑n)
this : f x = f (x - 1) + log (x - 1)
⊢ f (x - 1) + log (x - 1) - f 1 = f (x - 1) - f 1 + log (x - 1) - x * 0