Dual cone and polar cone are closely related concepts in convex analysis, a branch of mathematics.
Dual cone
In a vector space
The dual cone C* of a subset C in a linear space X over the reals, e.g. Euclidean space Rn, with dual space X* is the set
C
∗
=
{
y
∈
X
∗
:
⟨
y
,
x
⟩
≥
0
∀
x
∈
C
}
,
{\displaystyle C^{*}=\left\{y\in X^{*}:\langle y,x\rangle \geq 0\quad \forall x\in C\right\},}
where
⟨
y
,
x
⟩
{\displaystyle \langle y,x\rangle }
is the duality pairing between X and X*, i.e.
⟨
y
,
x
⟩
=
y
(
x
)
{\displaystyle \langle y,x\rangle =y(x)}
.
The set
C
∗
{\displaystyle C^{*}}
is always a convex cone, even if
C
{\displaystyle C}
is neither convex nor a cone.
In a topological vector space
If X is a topological vector space over the real or complex numbers, then the dual cone of a subset C ⊆ X is the following set of continuous linear functionals on X:
C
′
:=
{
f
∈
X
′
:
Re
(
f
(
x
)
)
≥
0
for all
x
∈
C
}
{\displaystyle C^{\prime }:=\left\{f\in X^{\prime }:\operatorname {Re} \left(f(x)\right)\geq 0{\text{ for all }}x\in C\right\}}
,
which is the polar of the set -C.
No matter what C is,
C
′
{\displaystyle C^{\prime }}
will be a convex cone.
If C ⊆ {0} then
C
′
=
X
′
{\displaystyle C^{\prime }=X^{\prime }}
.
In a Hilbert space (internal dual cone)
Alternatively, many authors define the dual cone in the context of a real Hilbert space (such as Rn equipped with the Euclidean inner product) to be what is sometimes called the internal dual cone.
C
internal
∗
:=
{
y
∈
X
:
⟨
y
,
x
⟩
≥
0
∀
x
∈
C
}
.
{\displaystyle C_{\text{internal}}^{*}:=\left\{y\in X:\langle y,x\rangle \geq 0\quad \forall x\in C\right\}.}
Properties
Using this latter definition for C*, we have that when C is a cone, the following properties hold:
A non-zero vector y is in C* if and only if both of the following conditions hold:
y is a normal at the origin of a hyperplane that supports C.
y and C lie on the same side of that supporting hyperplane.
C* is closed and convex.
C
1
⊆
C
2
{\displaystyle C_{1}\subseteq C_{2}}
implies
C
2
∗
⊆
C
1
∗
{\displaystyle C_{2}^{*}\subseteq C_{1}^{*}}
.
If C has nonempty interior, then C* is pointed, i.e. C* contains no line in its entirety.
If C is a cone and the closure of C is pointed, then C* has nonempty interior.
C** is the closure of the smallest convex cone containing C (a consequence of the hyperplane separation theorem)
Self-dual cones
A cone C in a vector space X is said to be self-dual if X can be equipped with an inner product ⟨⋅,⋅⟩ such that the internal dual cone relative to this inner product is equal to C.
Those authors who define the dual cone as the internal dual cone in a real Hilbert space usually say that a cone is self-dual if it is equal to its internal dual.
This is slightly different from the above definition, which permits a change of inner product.
For instance, the above definition makes a cone in Rn with ellipsoidal base self-dual, because the inner product can be changed to make the base spherical, and a cone with spherical base in Rn is equal to its internal dual.
The nonnegative orthant of Rn and the space of all positive semidefinite matrices are self-dual, as are the cones with ellipsoidal base (often called "spherical cones", "Lorentz cones", or sometimes "ice-cream cones").
So are all cones in R3 whose base is the convex hull of a regular polygon with an odd number of vertices.
A less regular example is the cone in R3 whose base is the "house": the convex hull of a square and a point outside the square forming an equilateral triangle (of the appropriate height) with one of the sides of the square.
Polar cone
For a set C in X, the polar cone of C is the set
C
o
=
{
y
∈
X
∗
:
⟨
y
,
x
⟩
≤
0
∀
x
∈
C
}
.
{\displaystyle C^{o}=\left\{y\in X^{*}:\langle y,x\rangle \leq 0\quad \forall x\in C\right\}.}
It can be seen that the polar cone is equal to the negative of the dual cone, i.e. Co = −C*.
For a closed convex cone C in X, the polar cone is equivalent to the polar set for C.
See also
Bipolar theorem
Polar set
References
Bibliography
Boltyanski, V. G.; Martini, H.; Soltan, P. (1997). Excursions into combinatorial geometry. New York: Springer. ISBN 3-540-61341-2.{{cite book}}: CS1 maint: publisher location (link)
Goh, C. J.; Yang, X.Q. (2002). Duality in optimization and variational inequalities. London; New York: Taylor & Francis. ISBN 0-415-27479-6.
Narici, Lawrence; Beckenstein, Edward (2011). Topological Vector Spaces. Pure and applied mathematics (Second ed.). Boca Raton, FL: CRC Press. ISBN 978-1584888666. OCLC 144216834.
Ramm, A.G. (2000). Shivakumar, P.N.; Strauss, A.V. (eds.). Operator theory and its applications. Providence, R.I.: American Mathematical Society. ISBN 0-8218-1990-9.
Schaefer, Helmut H.; Wolff, Manfred P. (1999). Topological Vector Spaces. GTM. Vol. 8 (Second ed.). New York, NY: Springer New York Imprint Springer. ISBN 978-1-4612-7155-0. OCLC 840278135.