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Convex Optimization - Polar Cone
Let S be a non empty set in Rn Then, the polar cone of S denoted by S∗ is given by S∗={p∈Rn,pTx≤0∀x∈S}.
Remark
Polar cone is always convex even if S is not convex.
If S is empty set, S∗=Rn.
Polarity may be seen as a generalisation of orthogonality.
Let C⊆Rn then the orthogonal space of C, denoted by C⊥={y∈Rn:⟨x,y⟩=0∀x∈C}.
Lemma
Let S,S1 and S2 be non empty sets in Rn then the following statements are true −
S∗ is a closed convex cone.
S⊆S∗∗ where S∗∗ is a polar cone of S∗.
S1⊆S2⇒S∗2⊆S∗1.
Proof
Step 1 − S∗={p∈Rn,pTx≤0∀x∈S}
-
Let x1,x2∈S∗⇒xT1x≤0 and xT2x≤0,∀x∈S
For λ∈(0,1),[λx1+(1−λ)x2]Tx=[(λx1)T+{(1−λ)x2}T]x,∀x∈S
=[λxT1+(1−λ)xT2]x=λxT1x+(1−λ)xT2≤0
Thus λx1+(1−λ)x2∈S∗
Therefore S∗ is a convex set.
-
For λ≥0,pTx≤0,∀x∈S
Therefore, λpTx≤0,
⇒(λp)Tx≤0
⇒λp∈S∗
Thus, S∗ is a cone.
-
To show S∗ is closed, i.e., to show if pn→p as n→∞, then p∈S∗
∀x∈S,pTnx−pTx=(pn−p)Tx
As pn→p as n→∞⇒(pn→p)→0
Therefore pTnx→pTx. But pTnx≤0,∀x∈S
Thus, pTx≤0,∀x∈S
⇒p∈S∗
Hence, S∗ is closed.
Step 2 − S∗∗={q∈Rn:qTp≤0,∀p∈S∗}
Let x∈S, then ∀p∈S∗,pTx≤0⇒xTp≤0⇒x∈S∗∗
Thus, S⊆S∗∗
Step 3 − S∗2={p∈Rn:pTx≤0,∀x∈S2}
Since S1⊆S2⇒∀x∈S2⇒∀x∈S1
Therefore, if ˆp∈S∗2,then ˆpTx≤0,∀x∈S2
⇒ˆpTx≤0,∀x∈S1
⇒ˆpT∈S∗1
⇒S∗2⊆S∗1
Theorem
Let C be a non empty closed convex cone, then C=C∗∗
Proof
C=C∗∗ by previous lemma.
To prove : x∈C∗∗⊆C
Let x∈C∗∗ and let x∉C
Then by fundamental separation theorem, there exists a vector p≠0 and a scalar α such that pTy≤α,∀y∈C
Therefore, pTx>α
But since (y=0)∈C and pTy≤α,∀y∈C⇒α≥0 and pTx>0
If p∉C∗, then there exists some ˉy∈C such that pTˉy>0 and pT(λˉy) can be made arbitrarily large by taking λ sufficiently large.
This contradicts with the fact that pTy≤α,∀y∈C
Therefore,p∈C∗
Since x∈C∗={q:qTp≤0,∀p∈C∗}
Therefore, xTp≤0⇒pTx≤0
But pTx>α
Thus is contardiction.
Thus, x∈C
Hence C=C∗∗.