l3lab/ntp-mathlib huggingface dataset:
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f' < C`, then
`f` grows slower than `C * x` on `D`, i.e., `f y - f x < C * (y - x)` whenever `x, y ∈ D`,
`x < y`. -/
theorem Convex.image_sub_lt_mul_sub_of_deriv_lt {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D)) {C}
    (lt_hf' : ∀ x ∈ interior D, deriv f x < C) (x : ℝ) (hx : x ∈ D) (y : ℝ) (hy : y ∈ D)
    (hxy : x < y) : f y - f x < C * (y - x) :=
  have hf'_gt : ∀ x ∈ interior D, -C < deriv (fun y => -f y) x := fun x hx => by
    

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
C : ℝ
lt_hf' : ∀ x ∈ interior D, deriv f x < C
x✝ : ℝ
hx✝ : x✝ ∈ D
y : ℝ
hy : y ∈ D
hxy : x✝ < y
x : ℝ
hx : x ∈ interior D
⊢ -C < deriv (fun y => -f y) x

Next tactic:
rw [deriv.neg, neg_lt_neg_iff]
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f' < C`, then
`f` grows slower than `C * x` on `D`, i.e., `f y - f x < C * (y - x)` whenever `x, y ∈ D`,
`x < y`. -/
theorem Convex.image_sub_lt_mul_sub_of_deriv_lt {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D)) {C}
    (lt_hf' : ∀ x ∈ interior D, deriv f x < C) (x : ℝ) (hx : x ∈ D) (y : ℝ) (hy : y ∈ D)
    (hxy : x < y) : f y - f x < C * (y - x) :=
  have hf'_gt : ∀ x ∈ interior D, -C < deriv (fun y => -f y) x := fun x hx => by
    rw [deriv.neg, neg_lt_neg_iff]
    

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
C : ℝ
lt_hf' : ∀ x ∈ interior D, deriv f x < C
x✝ : ℝ
hx✝ : x✝ ∈ D
y : ℝ
hy : y ∈ D
hxy : x✝ < y
x : ℝ
hx : x ∈ interior D
⊢ deriv (fun y => f y) x < C

Next tactic:
exact lt_hf' x hx
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f' < C`, then
`f` grows slower than `C * x` on `D`, i.e., `f y - f x < C * (y - x)` whenever `x, y ∈ D`,
`x < y`. -/
theorem Convex.image_sub_lt_mul_sub_of_deriv_lt {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D)) {C}
    (lt_hf' : ∀ x ∈ interior D, deriv f x < C) (x : ℝ) (hx : x ∈ D) (y : ℝ) (hy : y ∈ D)
    (hxy : x < y) : f y - f x < C * (y - x) :=
  have hf'_gt : ∀ x ∈ interior D, -C < deriv (fun y => -f y) x := fun x hx => by
    rw [deriv.neg, neg_lt_neg_iff]
    exact lt_hf' x hx
  by 

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
C : ℝ
lt_hf' : ∀ x ∈ interior D, deriv f x < C
x : ℝ
hx : x ∈ D
y : ℝ
hy : y ∈ D
hxy : x < y
hf'_gt : ∀ x ∈ interior D, -C < deriv (fun y => -f y) x
⊢ f y - f x < C * (y - x)

Next tactic:
linarith [hD.mul_sub_lt_image_sub_of_lt_deriv hf.neg hf'.neg hf'_gt x hx y hy hxy]
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f' ≤ C`, then
`f` grows at most as fast as `C * x` on `D`, i.e., `f y - f x ≤ C * (y - x)` whenever `x, y ∈ D`,
`x ≤ y`. -/
theorem Convex.image_sub_le_mul_sub_of_deriv_le {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D)) {C}
    (le_hf' : ∀ x ∈ interior D, deriv f x ≤ C) (x : ℝ) (hx : x ∈ D) (y : ℝ) (hy : y ∈ D)
    (hxy : x ≤ y) : f y - f x ≤ C * (y - x) :=
  have hf'_ge : ∀ x ∈ interior D, -C ≤ deriv (fun y => -f y) x := fun x hx => by
    

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
C : ℝ
le_hf' : ∀ x ∈ interior D, deriv f x ≤ C
x✝ : ℝ
hx✝ : x✝ ∈ D
y : ℝ
hy : y ∈ D
hxy : x✝ ≤ y
x : ℝ
hx : x ∈ interior D
⊢ -C ≤ deriv (fun y => -f y) x

Next tactic:
rw [deriv.neg, neg_le_neg_iff]
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f' ≤ C`, then
`f` grows at most as fast as `C * x` on `D`, i.e., `f y - f x ≤ C * (y - x)` whenever `x, y ∈ D`,
`x ≤ y`. -/
theorem Convex.image_sub_le_mul_sub_of_deriv_le {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D)) {C}
    (le_hf' : ∀ x ∈ interior D, deriv f x ≤ C) (x : ℝ) (hx : x ∈ D) (y : ℝ) (hy : y ∈ D)
    (hxy : x ≤ y) : f y - f x ≤ C * (y - x) :=
  have hf'_ge : ∀ x ∈ interior D, -C ≤ deriv (fun y => -f y) x := fun x hx => by
    rw [deriv.neg, neg_le_neg_iff]
    

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
C : ℝ
le_hf' : ∀ x ∈ interior D, deriv f x ≤ C
x✝ : ℝ
hx✝ : x✝ ∈ D
y : ℝ
hy : y ∈ D
hxy : x✝ ≤ y
x : ℝ
hx : x ∈ interior D
⊢ deriv (fun y => f y) x ≤ C

Next tactic:
exact le_hf' x hx
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f' ≤ C`, then
`f` grows at most as fast as `C * x` on `D`, i.e., `f y - f x ≤ C * (y - x)` whenever `x, y ∈ D`,
`x ≤ y`. -/
theorem Convex.image_sub_le_mul_sub_of_deriv_le {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D)) {C}
    (le_hf' : ∀ x ∈ interior D, deriv f x ≤ C) (x : ℝ) (hx : x ∈ D) (y : ℝ) (hy : y ∈ D)
    (hxy : x ≤ y) : f y - f x ≤ C * (y - x) :=
  have hf'_ge : ∀ x ∈ interior D, -C ≤ deriv (fun y => -f y) x := fun x hx => by
    rw [deriv.neg, neg_le_neg_iff]
    exact le_hf' x hx
  by 

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
C : ℝ
le_hf' : ∀ x ∈ interior D, deriv f x ≤ C
x : ℝ
hx : x ∈ D
y : ℝ
hy : y ∈ D
hxy : x ≤ y
hf'_ge : ∀ x ∈ interior D, -C ≤ deriv (fun y => -f y) x
⊢ f y - f x ≤ C * (y - x)

Next tactic:
linarith [hD.mul_sub_le_image_sub_of_le_deriv hf.neg hf'.neg hf'_ge x hx y hy hxy]
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f'` is positive, then
`f` is a strictly monotone function on `D`.
Note that we don't require differentiability explicitly as it already implied by the derivative
being strictly positive. -/
theorem Convex.strictMonoOn_of_deriv_pos {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : ∀ x ∈ interior D, 0 < deriv f x) : StrictMonoOn f D := by
  

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : ∀ x ∈ interior D, 0 < deriv f x
⊢ StrictMonoOn f D

Next tactic:
intro x hx y hy
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f'` is positive, then
`f` is a strictly monotone function on `D`.
Note that we don't require differentiability explicitly as it already implied by the derivative
being strictly positive. -/
theorem Convex.strictMonoOn_of_deriv_pos {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : ∀ x ∈ interior D, 0 < deriv f x) : StrictMonoOn f D := by
  intro x hx y hy
  

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : ∀ x ∈ interior D, 0 < deriv f x
x : ℝ
hx : x ∈ D
y : ℝ
hy : y ∈ D
⊢ x < y → f x < f y

Next tactic:
have : DifferentiableOn ℝ f (interior D) := fun z hz =>
    (differentiableAt_of_deriv_ne_zero (hf' z hz).ne').differentiableWithinAt
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f'` is positive, then
`f` is a strictly monotone function on `D`.
Note that we don't require differentiability explicitly as it already implied by the derivative
being strictly positive. -/
theorem Convex.strictMonoOn_of_deriv_pos {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : ∀ x ∈ interior D, 0 < deriv f x) : StrictMonoOn f D := by
  intro x hx y hy
  have : DifferentiableOn ℝ f (interior D) := fun z hz =>
    (differentiableAt_of_deriv_ne_zero (hf' z hz).ne').differentiableWithinAt
  

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : ∀ x ∈ interior D, 0 < deriv f x
x : ℝ
hx : x ∈ D
y : ℝ
hy : y ∈ D
this : DifferentiableOn ℝ f (interior D)
⊢ x < y → f x < f y

Next tactic:
simpa only [zero_mul, sub_pos] using
    hD.mul_sub_lt_image_sub_of_lt_deriv hf this hf' x hx y hy
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f'` is nonnegative, then
`f` is a monotone function on `D`. -/
theorem Convex.monotoneOn_of_deriv_nonneg {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_nonneg : ∀ x ∈ interior D, 0 ≤ deriv f x) : MonotoneOn f D := fun x hx y hy hxy => by
  

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_nonneg : ∀ x ∈ interior D, 0 ≤ deriv f x
x : ℝ
hx : x ∈ D
y : ℝ
hy : y ∈ D
hxy : x ≤ y
⊢ f x ≤ f y

Next tactic:
simpa only [zero_mul, sub_nonneg] using
    hD.mul_sub_le_image_sub_of_le_deriv hf hf' hf'_nonneg x hx y hy hxy
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f'` is negative, then
`f` is a strictly antitone function on `D`. -/
theorem Convex.strictAntiOn_of_deriv_neg {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : ∀ x ∈ interior D, deriv f x < 0) : StrictAntiOn f D :=
  fun x hx y => by
  

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : ∀ x ∈ interior D, deriv f x < 0
x : ℝ
hx : x ∈ D
y : ℝ
⊢ y ∈ D → x < y → f y < f x

Next tactic:
simpa only [zero_mul, sub_lt_zero] using
    hD.image_sub_lt_mul_sub_of_deriv_lt hf
      (fun z hz => (differentiableAt_of_deriv_ne_zero (hf' z hz).ne).differentiableWithinAt) hf' x
      hx y
/-- Let `f` be a function continuous on a convex (or, equivalently, connected) subset `D`
of the real line. If `f` is differentiable on the interior of `D` and `f'` is nonpositive, then
`f` is an antitone function on `D`. -/
theorem Convex.antitoneOn_of_deriv_nonpos {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_nonpos : ∀ x ∈ interior D, deriv f x ≤ 0) : AntitoneOn f D := fun x hx y hy hxy => by
  

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_nonpos : ∀ x ∈ interior D, deriv f x ≤ 0
x : ℝ
hx : x ∈ D
y : ℝ
hy : y ∈ D
hxy : x ≤ y
⊢ f y ≤ f x

Next tactic:
simpa only [zero_mul, sub_nonpos] using
    hD.image_sub_le_mul_sub_of_deriv_le hf hf' hf'_nonpos x hx y hy hxy
/-- If a function `f` is continuous on a convex set `D ⊆ ℝ`, is differentiable on its interior,
and `f'` is monotone on the interior, then `f` is convex on `D`. -/
theorem MonotoneOn.convexOn_of_deriv {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_mono : MonotoneOn (deriv f) (interior D)) : ConvexOn ℝ D f :=
  convexOn_of_slope_mono_adjacent hD
    (by
      

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
⊢ ∀ {x y z : ℝ}, x ∈ D → z ∈ D → x < y → y < z → (f y - f x) / (y - x) ≤ (f z - f y) / (z - y)

Next tactic:
intro x y z hx hz hxy hyz
/-- If a function `f` is continuous on a convex set `D ⊆ ℝ`, is differentiable on its interior,
and `f'` is monotone on the interior, then `f` is convex on `D`. -/
theorem MonotoneOn.convexOn_of_deriv {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_mono : MonotoneOn (deriv f) (interior D)) : ConvexOn ℝ D f :=
  convexOn_of_slope_mono_adjacent hD
    (by
      intro x y z hx hz hxy hyz
      -- First we prove some trivial inclusions
      

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
x y z : ℝ
hx : x ∈ D
hz : z ∈ D
hxy : x < y
hyz : y < z
⊢ (f y - f x) / (y - x) ≤ (f z - f y) / (z - y)

Next tactic:
have hxzD : Icc x z ⊆ D := hD.ordConnected.out hx hz
/-- If a function `f` is continuous on a convex set `D ⊆ ℝ`, is differentiable on its interior,
and `f'` is monotone on the interior, then `f` is convex on `D`. -/
theorem MonotoneOn.convexOn_of_deriv {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_mono : MonotoneOn (deriv f) (interior D)) : ConvexOn ℝ D f :=
  convexOn_of_slope_mono_adjacent hD
    (by
      intro x y z hx hz hxy hyz
      -- First we prove some trivial inclusions
      have hxzD : Icc x z ⊆ D := hD.ordConnected.out hx hz
      

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
x y z : ℝ
hx : x ∈ D
hz : z ∈ D
hxy : x < y
hyz : y < z
hxzD : Icc x z ⊆ D
⊢ (f y - f x) / (y - x) ≤ (f z - f y) / (z - y)

Next tactic:
have hxyD : Icc x y ⊆ D := (Icc_subset_Icc_right hyz.le).trans hxzD
/-- If a function `f` is continuous on a convex set `D ⊆ ℝ`, is differentiable on its interior,
and `f'` is monotone on the interior, then `f` is convex on `D`. -/
theorem MonotoneOn.convexOn_of_deriv {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_mono : MonotoneOn (deriv f) (interior D)) : ConvexOn ℝ D f :=
  convexOn_of_slope_mono_adjacent hD
    (by
      intro x y z hx hz hxy hyz
      -- First we prove some trivial inclusions
      have hxzD : Icc x z ⊆ D := hD.ordConnected.out hx hz
      have hxyD : Icc x y ⊆ D := (Icc_subset_Icc_right hyz.le).trans hxzD
      

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
x y z : ℝ
hx : x ∈ D
hz : z ∈ D
hxy : x < y
hyz : y < z
hxzD : Icc x z ⊆ D
hxyD : Icc x y ⊆ D
⊢ (f y - f x) / (y - x) ≤ (f z - f y) / (z - y)

Next tactic:
have hxyD' : Ioo x y ⊆ interior D :=
        subset_sUnion_of_mem ⟨isOpen_Ioo, Ioo_subset_Icc_self.trans hxyD⟩
/-- If a function `f` is continuous on a convex set `D ⊆ ℝ`, is differentiable on its interior,
and `f'` is monotone on the interior, then `f` is convex on `D`. -/
theorem MonotoneOn.convexOn_of_deriv {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_mono : MonotoneOn (deriv f) (interior D)) : ConvexOn ℝ D f :=
  convexOn_of_slope_mono_adjacent hD
    (by
      intro x y z hx hz hxy hyz
      -- First we prove some trivial inclusions
      have hxzD : Icc x z ⊆ D := hD.ordConnected.out hx hz
      have hxyD : Icc x y ⊆ D := (Icc_subset_Icc_right hyz.le).trans hxzD
      have hxyD' : Ioo x y ⊆ interior D :=
        subset_sUnion_of_mem ⟨isOpen_Ioo, Ioo_subset_Icc_self.trans hxyD⟩
      

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
x y z : ℝ
hx : x ∈ D
hz : z ∈ D
hxy : x < y
hyz : y < z
hxzD : Icc x z ⊆ D
hxyD : Icc x y ⊆ D
hxyD' : Ioo x y ⊆ interior D
⊢ (f y - f x) / (y - x) ≤ (f z - f y) / (z - y)

Next tactic:
have hyzD : Icc y z ⊆ D := (Icc_subset_Icc_left hxy.le).trans hxzD
/-- If a function `f` is continuous on a convex set `D ⊆ ℝ`, is differentiable on its interior,
and `f'` is monotone on the interior, then `f` is convex on `D`. -/
theorem MonotoneOn.convexOn_of_deriv {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_mono : MonotoneOn (deriv f) (interior D)) : ConvexOn ℝ D f :=
  convexOn_of_slope_mono_adjacent hD
    (by
      intro x y z hx hz hxy hyz
      -- First we prove some trivial inclusions
      have hxzD : Icc x z ⊆ D := hD.ordConnected.out hx hz
      have hxyD : Icc x y ⊆ D := (Icc_subset_Icc_right hyz.le).trans hxzD
      have hxyD' : Ioo x y ⊆ interior D :=
        subset_sUnion_of_mem ⟨isOpen_Ioo, Ioo_subset_Icc_self.trans hxyD⟩
      have hyzD : Icc y z ⊆ D := (Icc_subset_Icc_left hxy.le).trans hxzD
      

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
x y z : ℝ
hx : x ∈ D
hz : z ∈ D
hxy : x < y
hyz : y < z
hxzD : Icc x z ⊆ D
hxyD : Icc x y ⊆ D
hxyD' : Ioo x y ⊆ interior D
hyzD : Icc y z ⊆ D
⊢ (f y - f x) / (y - x) ≤ (f z - f y) / (z - y)

Next tactic:
have hyzD' : Ioo y z ⊆ interior D :=
        subset_sUnion_of_mem ⟨isOpen_Ioo, Ioo_subset_Icc_self.trans hyzD⟩
/-- If a function `f` is continuous on a convex set `D ⊆ ℝ`, is differentiable on its interior,
and `f'` is monotone on the interior, then `f` is convex on `D`. -/
theorem MonotoneOn.convexOn_of_deriv {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_mono : MonotoneOn (deriv f) (interior D)) : ConvexOn ℝ D f :=
  convexOn_of_slope_mono_adjacent hD
    (by
      intro x y z hx hz hxy hyz
      -- First we prove some trivial inclusions
      have hxzD : Icc x z ⊆ D := hD.ordConnected.out hx hz
      have hxyD : Icc x y ⊆ D := (Icc_subset_Icc_right hyz.le).trans hxzD
      have hxyD' : Ioo x y ⊆ interior D :=
        subset_sUnion_of_mem ⟨isOpen_Ioo, Ioo_subset_Icc_self.trans hxyD⟩
      have hyzD : Icc y z ⊆ D := (Icc_subset_Icc_left hxy.le).trans hxzD
      have hyzD' : Ioo y z ⊆ interior D :=
        subset_sUnion_of_mem ⟨isOpen_Ioo, Ioo_subset_Icc_self.trans hyzD⟩
      -- Then we apply MVT to both `[x, y]` and `[y, z]`
      

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
x y z : ℝ
hx : x ∈ D
hz : z ∈ D
hxy : x < y
hyz : y < z
hxzD : Icc x z ⊆ D
hxyD : Icc x y ⊆ D
hxyD' : Ioo x y ⊆ interior D
hyzD : Icc y z ⊆ D
hyzD' : Ioo y z ⊆ interior D
⊢ (f y - f x) / (y - x) ≤ (f z - f y) / (z - y)

Next tactic:
obtain ⟨a, ⟨hxa, hay⟩, ha⟩ : ∃ a ∈ Ioo x y, deriv f a = (f y - f x) / (y - x)
/-- If a function `f` is continuous on a convex set `D ⊆ ℝ`, is differentiable on its interior,
and `f'` is monotone on the interior, then `f` is convex on `D`. -/
theorem MonotoneOn.convexOn_of_deriv {D : Set ℝ} (hD : Convex ℝ D) {f : ℝ → ℝ}
    (hf : ContinuousOn f D) (hf' : DifferentiableOn ℝ f (interior D))
    (hf'_mono : MonotoneOn (deriv f) (interior D)) : ConvexOn ℝ D f :=
  convexOn_of_slope_mono_adjacent hD
    (by
      intro x y z hx hz hxy hyz
      -- First we prove some trivial inclusions
      have hxzD : Icc x z ⊆ D := hD.ordConnected.out hx hz
      have hxyD : Icc x y ⊆ D := (Icc_subset_Icc_right hyz.le).trans hxzD
      have hxyD' : Ioo x y ⊆ interior D :=
        subset_sUnion_of_mem ⟨isOpen_Ioo, Ioo_subset_Icc_self.trans hxyD⟩
      have hyzD : Icc y z ⊆ D := (Icc_subset_Icc_left hxy.le).trans hxzD
      have hyzD' : Ioo y z ⊆ interior D :=
        subset_sUnion_of_mem ⟨isOpen_Ioo, Ioo_subset_Icc_self.trans hyzD⟩
      -- Then we apply MVT to both `[x, y]` and `[y, z]`
      obtain ⟨a, ⟨hxa, hay⟩, ha⟩ : ∃ a ∈ Ioo x y, deriv f a = (f y - f x) / (y - x)
      

Proof state:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
x y z : ℝ
hx : x ∈ D
hz : z ∈ D
hxy : x < y
hyz : y < z
hxzD : Icc x z ⊆ D
hxyD : Icc x y ⊆ D
hxyD' : Ioo x y ⊆ interior D
hyzD : Icc y z ⊆ D
hyzD' : Ioo y z ⊆ interior D
⊢ ∃ a ∈ Ioo x y, deriv f a = (f y - f x) / (y - x)
case intro.intro.intro
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
F : Type u_2
inst✝¹ : NormedAddCommGroup F
inst✝ : NormedSpace ℝ F
D : Set ℝ
hD : Convex ℝ D
f : ℝ → ℝ
hf : ContinuousOn f D
hf' : DifferentiableOn ℝ f (interior D)
hf'_mono : MonotoneOn (deriv f) (interior D)
x y z : ℝ
hx : x ∈ D
hz : z ∈ D
hxy : x < y
hyz : y < z
hxzD : Icc x z ⊆ D
hxyD : Icc x y ⊆ D
hxyD' : Ioo x y ⊆ interior D
hyzD : Icc y z ⊆ D
hyzD' : Ioo y z ⊆ interior D
a : ℝ
ha : deriv f a = (f y - f x) / (y - x)
hxa : x < a
hay : a < y
⊢ (f y - f x) / (y - x) ≤ (f z - f y) / (z - y)

Next tactic:
exact exists_deriv_eq_slope f hxy (hf.mono hxyD) (hf'.mono hxyD')