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Convex Analysis And OptimizationConvex Analysis And OptimizationChap.1 Basic Convexity Concepts Section1.4 Relative Interior, Closure, and ContinuityNaoki ItoJuly 8, 2012......Convex Analysis And Optimization Basic Convexity Concepts Relative Interior, Closure, and ContinuityThe proof of Proposition 1.4.1Proposition 1.4.1 C : Nonempty convex seta(Line Segment Principle) x ∈ ri(C), x ∈ cl(C) ⇒ αx + (1 − α)¯ ∈ ri(C) for ∀α ∈ (0, 1] ¯ xProof) (i)The case in x ∈ C ¯ Let
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. . . . . .
Convex Analysis And Optimization
Convex Analysis And OptimizationChap.1 Basic Convexity Concepts
Section1.4 Relative Interior, Closure, and Continuity
Naoki Ito
July 8, 2012
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of Proposition 1.4.1
Proposition 1.4.1
C : Nonempty convex set
a (Line Segment Principle)x ∈ ri(C), x ∈ cl(C) ⇒ αx+ (1− α)x ∈ ri(C) for ∀α ∈ (0, 1]
Proof)(i)The case in x ∈ CLet x = αx+ (1− α)x (α ∈ (0, 1])
Since x ∈ ri(C),∃δ > 0 s.t. B(x, δ) ∩ aff(C) ⊂ C
The each point of B(xα, αδ)∩ aff(C) is a convex combinationof x and some point of B(x, δ) ∩ aff(C)
Therefore, by the covexity of C, B(xα, αδ) ∩ aff(C) ⊂ C,implying that xα ∈ ri(C)
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of Proposition 1.4.1(a)(cont)
(ii)The case in x /∈ C
Let xk ⊂ C −→ x
Let xk,α = αx+ (1− α)xk (α ∈ (0, 1])xk,α → xα
As (i), we see that
∃δ > 0 s.t. B(x, δ) ∩ aff(C) ⊂ C
and B(xk,α, αδ) ∩ aff(C) ⊂ C for allk ≥ 0
Since xk,α → xα
∃N ≥ 0, ∀k ≥ N s.t. B
(xα,
αδ
2
)⊂ B(xk,α, αδ)
It follows B(xα,αδ2 ) ∩ aff(C) ⊂ C
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of Proposition 1.4.1(a)(cont)
(ii)The case in x /∈ C
Let xk ⊂ C −→ x
Let xk,α = αx+ (1− α)xk (α ∈ (0, 1])xk,α → xα
As (i), we see that
∃δ > 0 s.t. B(x, δ) ∩ aff(C) ⊂ C
and B(xk,α, αδ) ∩ aff(C) ⊂ C for allk ≥ 0
Since xk,α → xα
∃N ≥ 0, ∀k ≥ N s.t. B
(xα,
αδ
2
)⊂ B(xk,α, αδ)
It follows B(xα,αδ2 ) ∩ aff(C) ⊂ C
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of Proposition 1.4.1(a)(cont)
(ii)The case in x /∈ C
Let xk ⊂ C −→ x
Let xk,α = αx+ (1− α)xk (α ∈ (0, 1])xk,α → xα
As (i), we see that
∃δ > 0 s.t. B(x, δ) ∩ aff(C) ⊂ C
and B(xk,α, αδ) ∩ aff(C) ⊂ C for allk ≥ 0
Since xk,α → xα
∃N ≥ 0, ∀k ≥ N s.t. B
(xα,
αδ
2
)⊂ B(xk,α, αδ)
It follows B(xα,αδ2 ) ∩ aff(C) ⊂ C
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of Proposition 1.4.1(a)(cont)
(ii)The case in x /∈ C
Let xk ⊂ C −→ x
Let xk,α = αx+ (1− α)xk (α ∈ (0, 1])xk,α → xα
As (i), we see that
∃δ > 0 s.t. B(x, δ) ∩ aff(C) ⊂ C
and B(xk,α, αδ) ∩ aff(C) ⊂ C for allk ≥ 0
Since xk,α → xα
∃N ≥ 0, ∀k ≥ N s.t. B
(xα,
αδ
2
)⊂ B(xk,α, αδ)
It follows B(xα,αδ2 ) ∩ aff(C) ⊂ C
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
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Proposition 1.4.1(b)
Proposition 1.4.1
C : Nonempty convex set
b (Nonemptiness of Relative interior)ri(C) is a nonempty convex setaff(C)=aff(ri(C))
Furthermore...dim(aff(C)):= m
m > 0 ⇒ ∃x0, x1, ..., xm ∈ ri(C)
s.t. span{x1 − x0, ..., xm − x0}= {subspace parallel to aff(C)}
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of proposition 1.4.1(b)
Proof)(Convexity of ri(C))Convexity of ri(C) follows from the Line Segment Principle.
ri(C) ⊂ C ⊂ cl(C)
. . . . . .
Convex Analysis And Optimization
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The proof of proposition 1.4.1(b)(cont)
( Nonemptiness of ri(C) and aff(C)=aff(ri(C)) )We assume without loss of generality that 0 ∈ Cdim(aff(C)) := m
(i)The case in m = 0
C 6= φ → C = {0}aff(C) = {0}ri(C) = {0}, thus, ri(C) 6= φ
aff(ri(C)) = {0} thus, aff(C)=aff(ri(C))
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of proposition 1.4.1(b)(cont)
( Nonemptiness of ri(C) and aff(C)=aff(ri(C)) )We assume without loss of generality that 0 ∈ Cdim(aff(C)) := m
(i)The case in m = 0
C 6= φ → C = {0}
aff(C) = {0}ri(C) = {0}, thus, ri(C) 6= φ
aff(ri(C)) = {0} thus, aff(C)=aff(ri(C))
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of proposition 1.4.1(b)(cont)
( Nonemptiness of ri(C) and aff(C)=aff(ri(C)) )We assume without loss of generality that 0 ∈ Cdim(aff(C)) := m
(i)The case in m = 0
C 6= φ → C = {0}aff(C) = {0}
ri(C) = {0}, thus, ri(C) 6= φ
aff(ri(C)) = {0} thus, aff(C)=aff(ri(C))
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of proposition 1.4.1(b)(cont)
( Nonemptiness of ri(C) and aff(C)=aff(ri(C)) )We assume without loss of generality that 0 ∈ Cdim(aff(C)) := m
(i)The case in m = 0
C 6= φ → C = {0}aff(C) = {0}ri(C) = {0}, thus, ri(C) 6= φ
aff(ri(C)) = {0} thus, aff(C)=aff(ri(C))
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
Relative Interior, Closure, and Continuity
The proof of proposition 1.4.1(b)(cont)
( Nonemptiness of ri(C) and aff(C)=aff(ri(C)) )We assume without loss of generality that 0 ∈ Cdim(aff(C)) := m
(i)The case in m = 0
C 6= φ → C = {0}aff(C) = {0}ri(C) = {0}, thus, ri(C) 6= φ
aff(ri(C)) = {0} thus, aff(C)=aff(ri(C))
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
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The proof of proposition 1.4.1(b)(cont)
(ii)The case in m > 0
We can take m linearly independent vectorsz1, ..., zm ∈ C that span aff(C)
Consider the setX = {x|x =
∑mi=1 αizi,
∑mi=1 αi < 1, αi > 0 (i = 1, ...,m)}
( X is open. X ∈ C)
We will show
X is open relative to aff(C)
∀x ∈ X, ∃δ > 0 s.t. B(x, δ) ∩ aff(C) ⊂ X
. . . . . .
Convex Analysis And Optimization
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The proof of proposition 1.4.1(b)(cont)
(ii)The case in m > 0
We can take m linearly independent vectorsz1, ..., zm ∈ C that span aff(C)
Consider the setX = {x|x =
∑mi=1 αizi,
∑mi=1 αi < 1, αi > 0 (i = 1, ...,m)}
( X is open. X ∈ C)
We will show
X is open relative to aff(C)
∀x ∈ X, ∃δ > 0 s.t. B(x, δ) ∩ aff(C) ⊂ X
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
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The proof of proposition 1.4.1(b)(cont)
(ii)The case in m > 0
We can take m linearly independent vectorsz1, ..., zm ∈ C that span aff(C)
Consider the setX = {x|x =
∑mi=1 αizi,
∑mi=1 αi < 1, αi > 0 (i = 1, ...,m)}
( X is open. X ∈ C)
We will show
X is open relative to aff(C)
∀x ∈ X, ∃δ > 0 s.t. B(x, δ) ∩ aff(C) ⊂ X
. . . . . .
Convex Analysis And Optimization
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The proof of proposition 1.4.1(b)(cont)
Fix x ∈ X and let x ∈aff(C)We have unique m-dimensional vectors α, αsuch that x = Zα, x = Zα (where Z := [z1, ..., zm])
Since Z ′Z is symmetric and positive definite,we have for some γ > 0, which is independent of x and x,
‖x− x‖2 = (α− α)′Z ′Z(α− α) ≤ γ‖α− α‖2
Since x ∈ X, α lies in the open set
A =
{(α1, ..., αm)|
m∑i=1
αi < 1, αi > 0 (i = 1, ...,m)
}
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
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The proof of proposition 1.4.1(b)(cont)
Fix x ∈ X and let x ∈aff(C)We have unique m-dimensional vectors α, αsuch that x = Zα, x = Zα (where Z := [z1, ..., zm])
Since Z ′Z is symmetric and positive definite,we have for some γ > 0, which is independent of x and x,
‖x− x‖2 = (α− α)′Z ′Z(α− α) ≤ γ‖α− α‖2
Since x ∈ X, α lies in the open set
A =
{(α1, ..., αm)|
m∑i=1
αi < 1, αi > 0 (i = 1, ...,m)
}
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
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The proof of proposition 1.4.1(b)(cont)
Fix x ∈ X and let x ∈aff(C)We have unique m-dimensional vectors α, αsuch that x = Zα, x = Zα (where Z := [z1, ..., zm])
Since Z ′Z is symmetric and positive definite,we have for some γ > 0, which is independent of x and x,
‖x− x‖2 = (α− α)′Z ′Z(α− α) ≤ γ‖α− α‖2
Since x ∈ X, α lies in the open set
A =
{(α1, ..., αm)|
m∑i=1
αi < 1, αi > 0 (i = 1, ...,m)
}
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
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The proof of proposition 1.4.1(b)(cont)
Take the δ such that the α where γ‖α− α‖2 < δ belongs toA. (i.e. x = Zα belongs to X)
Such the δ can be constructed as below.Since A is open set, ∃ε > 0 s.t. B(α, ε) ⊂ Aδ := γε2 then, γ‖α− α‖2 < δ = γε2 ⇒ ‖α− α‖2 < ε2
...implying that x ∈ X
Hence X contains B(x)∩aff(C), so X is open relative toaff(C).
It follows that ∀x ∈ X, x ∈ ri(C), so that ri(C) 6= φ.
Also, aff(X) = aff(C) (since by construction),X ⊂ri(C)(⊂ C),we see that aff(ri(C))=ri(C).
. . . . . .
Convex Analysis And Optimization
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The proof of proposition 1.4.1(b)(cont)
Take the δ such that the α where γ‖α− α‖2 < δ belongs toA. (i.e. x = Zα belongs to X)
Such the δ can be constructed as below.Since A is open set, ∃ε > 0 s.t. B(α, ε) ⊂ Aδ := γε2 then, γ‖α− α‖2 < δ = γε2 ⇒ ‖α− α‖2 < ε2
...implying that x ∈ X
Hence X contains B(x)∩aff(C), so X is open relative toaff(C).
It follows that ∀x ∈ X, x ∈ ri(C), so that ri(C) 6= φ.
Also, aff(X) = aff(C) (since by construction),X ⊂ri(C)(⊂ C),we see that aff(ri(C))=ri(C).
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
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The proof of proposition 1.4.1(b)(cont)
Take the δ such that the α where γ‖α− α‖2 < δ belongs toA. (i.e. x = Zα belongs to X)
Such the δ can be constructed as below.Since A is open set, ∃ε > 0 s.t. B(α, ε) ⊂ Aδ := γε2 then, γ‖α− α‖2 < δ = γε2 ⇒ ‖α− α‖2 < ε2
...implying that x ∈ X
Hence X contains B(x)∩aff(C), so X is open relative toaff(C).
It follows that ∀x ∈ X, x ∈ ri(C), so that ri(C) 6= φ.
Also, aff(X) = aff(C) (since by construction),X ⊂ri(C)(⊂ C),we see that aff(ri(C))=ri(C).
. . . . . .
Convex Analysis And Optimization
Basic Convexity Concepts
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The proof of proposition 1.4.1(b)(cont)
Take the δ such that the α where γ‖α− α‖2 < δ belongs toA. (i.e. x = Zα belongs to X)
Such the δ can be constructed as below.Since A is open set, ∃ε > 0 s.t. B(α, ε) ⊂ Aδ := γε2 then, γ‖α− α‖2 < δ = γε2 ⇒ ‖α− α‖2 < ε2
...implying that x ∈ X
Hence X contains B(x)∩aff(C), so X is open relative toaff(C).
It follows that ∀x ∈ X, x ∈ ri(C), so that ri(C) 6= φ.
Also, aff(X) = aff(C) (since by construction),X ⊂ri(C)(⊂ C),we see that aff(ri(C))=ri(C).
. . . . . .
Convex Analysis And Optimization
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The proof of proposition 1.4.1(b)(cont)
Take the δ such that the α where γ‖α− α‖2 < δ belongs toA. (i.e. x = Zα belongs to X)
Such the δ can be constructed as below.Since A is open set, ∃ε > 0 s.t. B(α, ε) ⊂ Aδ := γε2 then, γ‖α− α‖2 < δ = γε2 ⇒ ‖α− α‖2 < ε2
...implying that x ∈ X
Hence X contains B(x)∩aff(C), so X is open relative toaff(C).
It follows that ∀x ∈ X, x ∈ ri(C), so that ri(C) 6= φ.
Also, aff(X) = aff(C) (since by construction),X ⊂ri(C)(⊂ C),we see that aff(ri(C))=ri(C).
. . . . . .
Convex Analysis And Optimization
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The proof of proposition 1.4.1(b)(cont)
Take the δ such that the α where γ‖α− α‖2 < δ belongs toA. (i.e. x = Zα belongs to X)
Such the δ can be constructed as below.Since A is open set, ∃ε > 0 s.t. B(α, ε) ⊂ Aδ := γε2 then, γ‖α− α‖2 < δ = γε2 ⇒ ‖α− α‖2 < ε2
...implying that x ∈ X
Hence X contains B(x)∩aff(C), so X is open relative toaff(C).
It follows that ∀x ∈ X, x ∈ ri(C), so that ri(C) 6= φ.
Also, aff(X) = aff(C) (since by construction),X ⊂ri(C)(⊂ C),we see that aff(ri(C))=ri(C).
. . . . . .
Convex Analysis And Optimization
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Proposition 1.4.1(b) (written again)
m > 0 ⇒ ∃x0, x1, ..., xm ∈ ri(C)
s.t. span{x1 − x0, ..., xm − x0}= {subspace parallel to aff(C)}
Proof)
consider vectors
x0 = α
m∑i=1
zi, xi = x0 + αzi, i = 1, ...,m
where α is positive scalar that α(m+ 1) < 1
xi ∈ X ⊂ ri(C) (i = 0, 1, ...,m)
xi − x0 = αzi (i = 0, 1, ...,m) span aff(C).
. . . . . .
Convex Analysis And Optimization
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Proposition 1.4.1(b) (written again)
m > 0 ⇒ ∃x0, x1, ..., xm ∈ ri(C)
s.t. span{x1 − x0, ..., xm − x0}= {subspace parallel to aff(C)}
Proof)
consider vectors
x0 = α
m∑i=1
zi, xi = x0 + αzi, i = 1, ...,m
where α is positive scalar that α(m+ 1) < 1
xi ∈ X ⊂ ri(C) (i = 0, 1, ...,m)
xi − x0 = αzi (i = 0, 1, ...,m) span aff(C).
. . . . . .
Convex Analysis And Optimization
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Proposition 1.4.1(b) (written again)
m > 0 ⇒ ∃x0, x1, ..., xm ∈ ri(C)
s.t. span{x1 − x0, ..., xm − x0}= {subspace parallel to aff(C)}
Proof)
consider vectors
x0 = α
m∑i=1
zi, xi = x0 + αzi, i = 1, ...,m
where α is positive scalar that α(m+ 1) < 1
xi ∈ X ⊂ ri(C) (i = 0, 1, ...,m)
xi − x0 = αzi (i = 0, 1, ...,m) span aff(C).
. . . . . .
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Proposition 1.4.1 (c)
Proposition 1.4.1
c x ∈ ri(C) ⇔ ∀x ∈ C, ∃γ > 1 s.t. x+(γ−1)(x− x) ∈ C
Proof)⇒) (trivial)⇐)
Since ri(C) 6= φ, there exists some x ∈ ri(C)
If x = x, we are done. So, we assume x 6= x.
By the given condition, since x ∈ C,there exists a γ > 1 s.t. y = x+ (γ − 1)(x− x) ∈ C
Thus, we have x = γ−1γ x+ 1
γyBy using Line Segment Principle, we obtain x ∈ ri(C)
. . . . . .
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Proposition 1.4.1 (c)
Proposition 1.4.1
c x ∈ ri(C) ⇔ ∀x ∈ C, ∃γ > 1 s.t. x+(γ−1)(x− x) ∈ C
Proof)⇒) (trivial)⇐)
Since ri(C) 6= φ, there exists some x ∈ ri(C)
If x = x, we are done. So, we assume x 6= x.
By the given condition, since x ∈ C,there exists a γ > 1 s.t. y = x+ (γ − 1)(x− x) ∈ C
Thus, we have x = γ−1γ x+ 1
γyBy using Line Segment Principle, we obtain x ∈ ri(C)
. . . . . .
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Proposition 1.4.1 (c)
Proposition 1.4.1
c x ∈ ri(C) ⇔ ∀x ∈ C, ∃γ > 1 s.t. x+(γ−1)(x− x) ∈ C
Proof)⇒) (trivial)⇐)
Since ri(C) 6= φ, there exists some x ∈ ri(C)
If x = x, we are done. So, we assume x 6= x.
By the given condition, since x ∈ C,there exists a γ > 1 s.t. y = x+ (γ − 1)(x− x) ∈ C
Thus, we have x = γ−1γ x+ 1
γyBy using Line Segment Principle, we obtain x ∈ ri(C)
. . . . . .
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Proposition 1.4.1 (c)
Proposition 1.4.1
c x ∈ ri(C) ⇔ ∀x ∈ C, ∃γ > 1 s.t. x+(γ−1)(x− x) ∈ C
Proof)⇒) (trivial)⇐)
Since ri(C) 6= φ, there exists some x ∈ ri(C)
If x = x, we are done. So, we assume x 6= x.
By the given condition, since x ∈ C,there exists a γ > 1 s.t. y = x+ (γ − 1)(x− x) ∈ C
Thus, we have x = γ−1γ x+ 1
γyBy using Line Segment Principle, we obtain x ∈ ri(C)
. . . . . .
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Proposition 1.4.2
X : Nonempty convex subset of Rn
f : X 7→ R , concaveX∗ := {x∗ ∈ X|f(x∗) = infx∈X f(x)}
X∗ ∩ ri(X) 6= φ ⇒ f must be constant over X, i.e., X∗ = X
. . . . . .
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Proposition 1.4.3
C, C: Nonempty convex sets.
a cl(C) = cl(ri(C))
b ri(C) = ri(cl(C))
c Following three conditions are equivalent.
i ri(C) = ri(C)ii cl(C) = cl(C)iii ri(C) ⊂ C ⊂cl(C)
. . . . . .
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Proposition 1.4.4
C : Nonempty convex subset of Rn
A : m× n matrix
a A · ri(C) = ri(A · C)
b A · cl(C) ⊂ cl(A · C)Furthermore, if C is boundedA · cl(C) = cl(A · C)
. . . . . .
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Proposition 1.4.5
C1, C2 : Nonempty convex sets
a ri(C1) ∩ ri(C2) ⊂ ri(C1 ∩ C2), cl(C1 ∩ C2) ⊂ cl(C1) ∩ ri(C2)Furthermore, if ri(C1) ∩ ri(C2) 6= φ, thenri(C1) ∩ ri(C2) = ri(C1 ∩ C2), cl(C1 ∩ C2) = cl(C1) ∩ ri(C2)
b ri(C1+C2) ⊂ ri(C1)+ ri(C2), cl(C1)+ cl(C2) ⊂ cl(C1+C2)Furthermore, if at least one of the sets C1 and C2 is bounded,then cl(C1) + cl(C2) = cl(C1 + C2)
. . . . . .
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Proposition 1.4.6
f : Rn 7→ R is convex ⇒ f is continuous.
f : Rn 7→ (−∞,∞] is a proper convex function⇒ f is continuous over the ri(dom(f)).