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Minkowski functional

In mathematics, in the field of functional analysis, a Minkowski functional or gauge function is a function that recovers a notion of distance on a linear space.

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In mathematics, in the field of functional analysis, a Minkowski functional (after Hermann Minkowski) or gauge function is a function that recovers a notion of distance on a linear space.

If K {\textstyle K} is a subset of a real or complex vector space X , {\textstyle X,} then the Minkowski functional or gauge of K {\textstyle K} is defined to be the function p K : X [ 0 , ] , {\textstyle p_{K}:X\to [0,\infty ],} valued in the extended real numbers, defined by p K ( x ) = inf { r R : r > 0  and  x r K } , x X , {\displaystyle p_{K}(x)=\inf\{r\in \mathbb {R} :r>0{\text{ and }}x\in rK\},\quad x\in X,} where the infimum of the empty set is defined to be positive infinity.

The set K {\textstyle K} is often assumed to have properties, such as being an absorbing disk in X {\textstyle X} , that guarantee that p K {\textstyle p_{K}} will be a seminorm on X . {\textstyle X.} In fact, every seminorm p {\textstyle p} on X {\textstyle X} is equal to the Minkowski functional (that is, p = p K {\textstyle p=p_{K}} ) of any subset K {\textstyle K} of X {\textstyle X} satisfying

{ x X : p ( x ) < 1 } K { x X : p ( x ) 1 } {\displaystyle \{x\in X:p(x)<1\}\subseteq K\subseteq \{x\in X:p(x)\leq 1\}}

(where all three of these sets are necessarily absorbing in X {\textstyle X} and the first and last are also disks).

Thus every seminorm (which is a function defined by purely algebraic properties) can be associated (non-uniquely) with an absorbing disk (which is a set with certain geometric properties) and conversely, every absorbing disk can be associated with its Minkowski functional (which will necessarily be a seminorm). These relationships between seminorms, Minkowski functionals, and absorbing disks is a major reason why Minkowski functionals are studied and used in functional analysis. In particular, through these relationships, Minkowski functionals allow one to "translate" certain geometric properties of a subset of X {\textstyle X} into certain algebraic properties of a function on X . {\textstyle X.}

The Minkowski function is always non-negative (meaning p K 0 {\textstyle p_{K}\geq 0} ). This property of being nonnegative stands in contrast to other classes of functions, such as sublinear functions and real linear functionals, that do allow negative values. However, p K {\textstyle p_{K}} might not be real-valued since for any given x X , {\textstyle x\in X,} the value p K ( x ) {\textstyle p_{K}(x)} is a real number if and only if { r > 0 : x r K } {\textstyle \{r>0:x\in rK\}} is not empty. Consequently, K {\textstyle K} is usually assumed to have properties (such as being absorbing in X , {\textstyle X,} for instance) that will guarantee that p K {\textstyle p_{K}} is real-valued.

Definition

Let K {\textstyle K} be a subset of a real or complex vector space X . {\textstyle X.} Define the gauge of K {\textstyle K} or the Minkowski functional associated with or induced by K {\textstyle K} as being the function p K : X [ 0 , ] , {\textstyle p_{K}:X\to [0,\infty ],} valued in the extended real numbers, defined by

p K ( x ) := inf { r > 0 : x r K } , {\displaystyle p_{K}(x):=\inf\{r>0:x\in rK\},}

(recall that the infimum of the empty set is {\textstyle \,\infty } , that is, inf = {\textstyle \inf \varnothing =\infty } ). Here, { r > 0 : x r K } {\textstyle \{r>0:x\in rK\}} is shorthand for { r R : r > 0  and  x r K } . {\textstyle \{r\in \mathbb {R} :r>0{\text{ and }}x\in rK\}.}

For any x X , {\textstyle x\in X,} p K ( x ) {\textstyle p_{K}(x)\neq \infty } if and only if { r > 0 : x r K } {\textstyle \{r>0:x\in rK\}} is not empty. The arithmetic operations on R {\textstyle \mathbb {R} } can be extended to operate on ± , {\textstyle \pm \infty ,} where r ± := 0 {\textstyle {\frac {r}{\pm \infty }}:=0} for all non-zero real < r < . {\textstyle -\infty <r<\infty .} The products 0 {\textstyle 0\cdot \infty } and 0 {\textstyle 0\cdot -\infty } remain undefined.

Some conditions making a gauge real-valued

In the field of convex analysis, the map p K {\textstyle p_{K}} taking on the value of {\textstyle \,\infty \,} is not necessarily an issue. However, in functional analysis p K {\textstyle p_{K}} is almost always real-valued (that is, to never take on the value of {\textstyle \,\infty \,} ), which happens if and only if the set { r > 0 : x r K } {\textstyle \{r>0:x\in rK\}} is non-empty for every x X . {\textstyle x\in X.}

In order for p K {\textstyle p_{K}} to be real-valued, it suffices for the origin of X {\textstyle X} to belong to the algebraic interior or core of K {\textstyle K} in X . {\textstyle X.} 1 If K {\textstyle K} is absorbing in X , {\textstyle X,} where recall that this implies that 0 K , {\textstyle 0\in K,} then the origin belongs to the algebraic interior of K {\textstyle K} in X {\textstyle X} and thus p K {\textstyle p_{K}} is real-valued. Characterizations of when p K {\textstyle p_{K}} is real-valued are given below.

Motivating examples

Example 1

Consider a normed vector space ( X , ) , {\textstyle (X,\|\,\cdot \,\|),} with the norm {\textstyle \|\,\cdot \,\|} and let U := { x X : x 1 } {\textstyle U:=\{x\in X:\|x\|\leq 1\}} be the unit ball in X . {\textstyle X.} Then for every x X , {\textstyle x\in X,} x = p U ( x ) . {\textstyle \|x\|=p_{U}(x).} Thus the Minkowski functional p U {\textstyle p_{U}} is just the norm on X . {\textstyle X.}

Example 2

Let X {\textstyle X} be a vector space without topology with underlying scalar field K . {\textstyle \mathbb {K} .} Let f : X K {\textstyle f:X\to \mathbb {K} } be any linear functional on X {\textstyle X} (not necessarily continuous). Fix a > 0. {\textstyle a>0.} Let K {\textstyle K} be the set K := { x X : | f ( x ) | a } {\displaystyle K:=\{x\in X:|f(x)|\leq a\}} and let p K {\textstyle p_{K}} be the Minkowski functional of K . {\textstyle K.} Then p K ( x ) = 1 a | f ( x ) |  for all  x X . {\displaystyle p_{K}(x)={\frac {1}{a}}|f(x)|\quad {\text{ for all }}x\in X.} The function p K {\textstyle p_{K}} has the following properties:

  1. It is subadditive: p K ( x + y ) p K ( x ) + p K ( y ) . {\textstyle p_{K}(x+y)\leq p_{K}(x)+p_{K}(y).}
  2. It is absolutely homogeneous: p K ( s x ) = | s | p K ( x ) {\textstyle p_{K}(sx)=|s|p_{K}(x)} for all scalars s . {\textstyle s.}
  3. It is nonnegative: p K 0. {\textstyle p_{K}\geq 0.}

Therefore, p K {\textstyle p_{K}} is a seminorm on X , {\textstyle X,} with an induced topology. This is characteristic of Minkowski functionals defined via "nice" sets. There is a one-to-one correspondence between seminorms and the Minkowski functional given by such sets. What is meant precisely by "nice" is discussed in the section below.

Notice that, in contrast to a stronger requirement for a norm, p K ( x ) = 0 {\textstyle p_{K}(x)=0} need not imply x = 0. {\textstyle x=0.} In the above example, one can take a nonzero x {\textstyle x} from the kernel of f . {\textstyle f.} Consequently, the resulting topology need not be Hausdorff.

Common conditions guaranteeing gauges are seminorms

To guarantee that p K ( 0 ) = 0 , {\textstyle p_{K}(0)=0,} it will henceforth be assumed that 0 K . {\textstyle 0\in K.}

In order for p K {\textstyle p_{K}} to be a seminorm, it suffices for K {\textstyle K} to be a disk (that is, convex and balanced) and absorbing in X , {\textstyle X,} which are the most common assumption placed on K . {\textstyle K.}

Theorem2If K {\textstyle K} is an absorbing disk in a vector space X {\textstyle X} then the Minkowski functional of K , {\textstyle K,} which is the map p K : X [ 0 , ) {\textstyle p_{K}:X\to [0,\infty )} defined by p K ( x ) := inf { r > 0 : x r K } , {\displaystyle p_{K}(x):=\inf\{r>0:x\in rK\},} is a seminorm on X . {\textstyle X.} Moreover, p K ( x ) = 1 sup { r > 0 : r x K } . {\displaystyle p_{K}(x)={\frac {1}{\sup\{r>0:rx\in K\}}}.}

More generally, if K {\textstyle K} is convex and the origin belongs to the algebraic interior of K , {\textstyle K,} then p K {\textstyle p_{K}} is a nonnegative sublinear functional on X , {\textstyle X,} which implies in particular that it is subadditive and positive homogeneous. If K {\textstyle K} is absorbing in X {\textstyle X} then p [ 0 , 1 ] K {\textstyle p_{[0,1]K}} is positive homogeneous, meaning that p [ 0 , 1 ] K ( s x ) = s p [ 0 , 1 ] K ( x ) {\textstyle p_{[0,1]K}(sx)=sp_{[0,1]K}(x)} for all real s 0 , {\textstyle s\geq 0,} where [ 0 , 1 ] K = { t k : t [ 0 , 1 ] , k K } . {\textstyle [0,1]K=\{tk:t\in [0,1],k\in K\}.} 3 If q {\textstyle q} is a nonnegative real-valued function on X {\textstyle X} that is positive homogeneous, then the sets U := { x X : q ( x ) < 1 } {\textstyle U:=\{x\in X:q(x)<1\}} and D := { x X : q ( x ) 1 } {\textstyle D:=\{x\in X:q(x)\leq 1\}} satisfy [ 0 , 1 ] U = U {\textstyle [0,1]U=U} and [ 0 , 1 ] D = D ; {\textstyle [0,1]D=D;} if in addition q {\textstyle q} is absolutely homogeneous then both U {\textstyle U} and D {\textstyle D} are balanced.3

Gauges of absorbing disks

Arguably the most common requirements placed on a set K {\textstyle K} to guarantee that p K {\textstyle p_{K}} is a seminorm are that K {\textstyle K} be an absorbing disk in X . {\textstyle X.} Due to how common these assumptions are, the properties of a Minkowski functional p K {\textstyle p_{K}} when K {\textstyle K} is an absorbing disk will now be investigated. Since all of the results mentioned above made few (if any) assumptions on K , {\textstyle K,} they can be applied in this special case.

TheoremAssume that K {\textstyle K} is an absorbing subset of X . {\textstyle X.} It is shown that:

  1. If K {\textstyle K} is convex then p K {\textstyle p_{K}} is subadditive.
  2. If K {\textstyle K} is balanced then p K {\textstyle p_{K}} is absolutely homogeneous; that is, p K ( s x ) = | s | p K ( x ) {\textstyle p_{K}(sx)=|s|p_{K}(x)} for all scalars s . {\textstyle s.}

Algebraic properties

Let X {\textstyle X} be a real or complex vector space and let K {\textstyle K} be an absorbing disk in X . {\textstyle X.}

  • p K {\textstyle p_{K}} is a seminorm on X . {\textstyle X.}
  • p K {\textstyle p_{K}} is a norm on X {\textstyle X} if and only if K {\textstyle K} does not contain a non-trivial vector subspace.4
  • p s K = 1 | s | p K {\textstyle p_{sK}={\frac {1}{|s|}}p_{K}} for any scalar s 0. {\textstyle s\neq 0.} 4
  • If J {\textstyle J} is an absorbing disk in X {\textstyle X} and J K {\textstyle J\subseteq K} then p K p J . {\textstyle p_{K}\leq p_{J}.}
  • If K {\textstyle K} is a set satisfying { x X : p ( x ) < 1 } K { x X : p ( x ) 1 } {\textstyle \{x\in X:p(x)<1\}\;\subseteq \;K\;\subseteq \;\{x\in X:p(x)\leq 1\}} then K {\textstyle K} is absorbing in X {\textstyle X} and p = p K , {\textstyle p=p_{K},} where p K {\textstyle p_{K}} is the Minkowski functional associated with K ; {\textstyle K;} that is, it is the gauge of K . {\textstyle K.} 5
  • In particular, if K {\textstyle K} is as above and q {\textstyle q} is any seminorm on X , {\textstyle X,} then q = p {\textstyle q=p} if and only if { x X : q ( x ) < 1 } K { x X : q ( x ) 1 } . {\textstyle \{x\in X:q(x)<1\}\;\subseteq \;K\;\subseteq \;\{x\in X:q(x)\leq 1\}.} 5
  • If x X {\textstyle x\in X} satisfies p K ( x ) < 1 {\textstyle p_{K}(x)<1} then x K . {\textstyle x\in K.}

Topological properties

Assume that X {\textstyle X} is a (real or complex) topological vector space (not necessarily Hausdorff or locally convex) and let K {\textstyle K} be an absorbing disk in X . {\textstyle X.} Then

Int X K { x X : p K ( x ) < 1 } K { x X : p K ( x ) 1 } Cl X K , {\displaystyle \operatorname {Int} _{X}K\;\subseteq \;\{x\in X:p_{K}(x)<1\}\;\subseteq \;K\;\subseteq \;\{x\in X:p_{K}(x)\leq 1\}\;\subseteq \;\operatorname {Cl} _{X}K,}

where Int X K {\textstyle \operatorname {Int} _{X}K} is the topological interior and Cl X K {\textstyle \operatorname {Cl} _{X}K} is the topological closure of K {\textstyle K} in X . {\textstyle X.} 6 Importantly, it was not assumed that p K {\textstyle p_{K}} was continuous nor was it assumed that K {\textstyle K} had any topological properties.

Moreover, the Minkowski functional p K {\textstyle p_{K}} is continuous if and only if K {\textstyle K} is a neighborhood of the origin in X . {\textstyle X.} 6 If p K {\textstyle p_{K}} is continuous then6 Int X K = { x X : p K ( x ) < 1 }  and  Cl X K = { x X : p K ( x ) 1 } . {\displaystyle \operatorname {Int} _{X}K=\{x\in X:p_{K}(x)<1\}\quad {\text{ and }}\quad \operatorname {Cl} _{X}K=\{x\in X:p_{K}(x)\leq 1\}.}

Minimal requirements on the set

This section will investigate the most general case of the gauge of any subset K {\textstyle K} of X . {\textstyle X.} The more common special case where K {\textstyle K} is assumed to be an absorbing disk in X {\textstyle X} was discussed above.

Properties

All results in this section may be applied to the case where K {\textstyle K} is an absorbing disk.

Throughout, K {\textstyle K} is any subset of X . {\textstyle X.}

SummarySuppose that K {\textstyle K} is a subset of a real or complex vector space X . {\textstyle X.}

  1. Strict positive homogeneity: p K ( r x ) = r p K ( x ) {\textstyle p_{K}(rx)=rp_{K}(x)} for all x X {\textstyle x\in X} and all positive real r > 0. {\textstyle r>0.}
    • Positive/Nonnegative homogeneity: p K {\textstyle p_{K}} is nonnegative homogeneous if and only if p K {\textstyle p_{K}} is real-valued.
      • A map p {\textstyle p} is called nonnegative homogeneous7 if p ( r x ) = r p ( x ) {\textstyle p(rx)=rp(x)} for all x X {\textstyle x\in X} and all nonnegative real r 0. {\textstyle r\geq 0.} Since 0 {\textstyle 0\cdot \infty } is undefined, a map that takes infinity as a value is not nonnegative homogeneous.
  2. Real-values: ( 0 , ) K {\textstyle (0,\infty )K} is the set of all points on which p K {\textstyle p_{K}} is real valued. So p K {\textstyle p_{K}} is real-valued if and only if ( 0 , ) K = X , {\textstyle (0,\infty )K=X,} in which case 0 K . {\textstyle 0\in K.}
    • Value at 0 {\textstyle 0} : p K ( 0 ) {\textstyle p_{K}(0)\neq \infty } if and only if 0 K {\textstyle 0\in K} if and only if p K ( 0 ) = 0. {\textstyle p_{K}(0)=0.}
    • Null space: If x X {\textstyle x\in X} then p K ( x ) = 0 {\textstyle p_{K}(x)=0} if and only if ( 0 , ) x ( 0 , 1 ) K {\textstyle (0,\infty )x\subseteq (0,1)K} if and only if there exists a divergent sequence of positive real numbers t 1 , t 2 , t 3 , {\textstyle t_{1},t_{2},t_{3},\cdots \to \infty } such that t n x K {\textstyle t_{n}x\in K} for all n . {\textstyle n.} Moreover, the zero set of p K {\textstyle p_{K}} is ker p K   = def   { y X : p K ( y ) = 0 } = e > 0 ( 0 , e ) K . {\textstyle \ker p_{K}~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~\left\{y\in X:p_{K}(y)=0\right\}={\textstyle \bigcap \limits _{e>0}}(0,e)K.}
  3. Comparison to a constant: If 0 r {\textstyle 0\leq r\leq \infty } then for any x X , {\textstyle x\in X,} p K ( x ) < r {\textstyle p_{K}(x)<r} if and only if x ( 0 , r ) K ; {\textstyle x\in (0,r)K;} this can be restated as: If 0 r {\textstyle 0\leq r\leq \infty } then p K 1 ( [ 0 , r ) ) = ( 0 , r ) K . {\textstyle p_{K}^{-1}([0,r))=(0,r)K.}
    • It follows that if 0 R < {\textstyle 0\leq R<\infty } is real then p K 1 ( [ 0 , R ] ) = e > 0 ( 0 , R + e ) K , {\textstyle p_{K}^{-1}([0,R])={\textstyle \bigcap \limits _{e>0}}(0,R+e)K,} where the set on the right hand side denotes e > 0 [ ( 0 , R + e ) K ] {\textstyle {\textstyle \bigcap \limits _{e>0}}[(0,R+e)K]} and not its subset [ e > 0 ( 0 , R + e ) ] K = ( 0 , R ] K . {\textstyle \left[{\textstyle \bigcap \limits _{e>0}}(0,R+e)\right]K=(0,R]K.} If R > 0 {\textstyle R>0} then these sets are equal if and only if K {\textstyle K} contains { y X : p K ( y ) = 1 } . {\textstyle \left\{y\in X:p_{K}(y)=1\right\}.}
    • In particular, if x R K {\textstyle x\in RK} or x ( 0 , R ] K {\textstyle x\in (0,R]K} then p K ( x ) R , {\textstyle p_{K}(x)\leq R,} but importantly, the converse is not necessarily true.
  4. Gauge comparison: For any subset L X , {\textstyle L\subseteq X,} p K p L {\textstyle p_{K}\leq p_{L}} if and only if ( 0 , 1 ) L ( 0 , 1 ) K ; {\textstyle (0,1)L\subseteq (0,1)K;} thus p L = p K {\textstyle p_{L}=p_{K}} if and only if ( 0 , 1 ) L = ( 0 , 1 ) K . {\textstyle (0,1)L=(0,1)K.}
    • The assignment L p L {\textstyle L\mapsto p_{L}} is order-reversing in the sense that if K L {\textstyle K\subseteq L} then p L p K . {\textstyle p_{L}\leq p_{K}.} 8
    • Because the set L := ( 0 , 1 ) K {\textstyle L:=(0,1)K} satisfies ( 0 , 1 ) L = ( 0 , 1 ) K , {\textstyle (0,1)L=(0,1)K,} it follows that replacing K {\textstyle K} with p K 1 ( [ 0 , 1 ) ) = ( 0 , 1 ) K {\textstyle p_{K}^{-1}([0,1))=(0,1)K} will not change the resulting Minkowski functional. The same is true of L := ( 0 , 1 ] K {\textstyle L:=(0,1]K} and of L := p K 1 ( [ 0 , 1 ] ) . {\textstyle L:=p_{K}^{-1}([0,1]).}
    • If D   = def   { y X : p K ( y ) = 1  or  p K ( y ) = 0 } {\textstyle D~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~\left\{y\in X:p_{K}(y)=1{\text{ or }}p_{K}(y)=0\right\}} then p D = p K {\textstyle p_{D}=p_{K}} and D {\textstyle D} has the particularly nice property that if r > 0 {\textstyle r>0} is real then x r D {\textstyle x\in rD} if and only if p D ( x ) = r {\textstyle p_{D}(x)=r} or p D ( x ) = 0. {\textstyle p_{D}(x)=0.} note 1 Moreover, if r > 0 {\textstyle r>0} is real then p D ( x ) r {\textstyle p_{D}(x)\leq r} if and only if x ( 0 , r ] D . {\textstyle x\in (0,r]D.}
  5. Subadditive/Triangle inequality: p K {\textstyle p_{K}} is subadditive if and only if ( 0 , 1 ) K {\textstyle (0,1)K} is convex. If K {\textstyle K} is convex then so are both ( 0 , 1 ) K {\textstyle (0,1)K} and ( 0 , 1 ] K {\textstyle (0,1]K} and moreover, p K {\textstyle p_{K}} is subadditive.
  6. Scaling the set: If s 0 {\textstyle s\neq 0} is a scalar then p s K ( y ) = p K ( 1 s y ) {\textstyle p_{sK}(y)=p_{K}\left({\tfrac {1}{s}}y\right)} for all y X . {\textstyle y\in X.} Thus if 0 < r < {\textstyle 0<r<\infty } is real then p r K ( y ) = p K ( 1 r y ) = 1 r p K ( y ) . {\textstyle p_{rK}(y)=p_{K}\left({\tfrac {1}{r}}y\right)={\tfrac {1}{r}}p_{K}(y).}
  7. Symmetric: p K {\textstyle p_{K}} is symmetric (meaning that p K ( y ) = p K ( y ) {\textstyle p_{K}(-y)=p_{K}(y)} for all y X {\textstyle y\in X} ) if and only if ( 0 , 1 ) K {\textstyle (0,1)K} is a symmetric set (meaning that ( 0 , 1 ) K = ( 0 , 1 ) K {\textstyle (0,1)K=-(0,1)K} ), which happens if and only if p K = p K . {\textstyle p_{K}=p_{-K}.}
  8. Absolute homogeneity: p K ( u x ) = p K ( x ) {\textstyle p_{K}(ux)=p_{K}(x)} for all x X {\textstyle x\in X} and all unit length scalars u {\textstyle u} note 2 if and only if ( 0 , 1 ) u K ( 0 , 1 ) K {\textstyle (0,1)uK\subseteq (0,1)K} for all unit length scalars u , {\textstyle u,} in which case p K ( s x ) = | s | p K ( x ) {\textstyle p_{K}(sx)=|s|p_{K}(x)} for all x X {\textstyle x\in X} and all non-zero scalars s 0. {\textstyle s\neq 0.} If in addition p K {\textstyle p_{K}} is also real-valued then this holds for all scalars s {\textstyle s} (that is, p K {\textstyle p_{K}} is absolutely homogeneousnote 3).
    • ( 0 , 1 ) u K ( 0 , 1 ) K {\textstyle (0,1)uK\subseteq (0,1)K} for all unit length u {\textstyle u} if and only if ( 0 , 1 ) u K = ( 0 , 1 ) K {\textstyle (0,1)uK=(0,1)K} for all unit length u . {\textstyle u.}
    • s K K {\textstyle sK\subseteq K} for all unit scalars s {\textstyle s} if and only if s K = K {\textstyle sK=K} for all unit scalars s ; {\textstyle s;} if this is the case then ( 0 , 1 ) K = ( 0 , 1 ) s K {\textstyle (0,1)K=(0,1)sK} for all unit scalars s . {\textstyle s.}
    • The Minkowski functional of any balanced set is a balanced function.8
  9. Absorbing: If K {\textstyle K} is convex or balanced and if ( 0 , ) K = X {\textstyle (0,\infty )K=X} then K {\textstyle K} is absorbing in X . {\textstyle X.}
    • If a set A {\textstyle A} is absorbing in X {\textstyle X} and A K {\textstyle A\subseteq K} then K {\textstyle K} is absorbing in X . {\textstyle X.}
    • If K {\textstyle K} is convex and 0 K {\textstyle 0\in K} then [ 0 , 1 ] K = K , {\textstyle [0,1]K=K,} in which case ( 0 , 1 ) K K . {\textstyle (0,1)K\subseteq K.}
  10. Restriction to a vector subspace: If S {\textstyle S} is a vector subspace of X {\textstyle X} and if p K S : S [ 0 , ] {\textstyle p_{K\cap S}:S\to [0,\infty ]} denotes the Minkowski functional of K S {\textstyle K\cap S} on S , {\textstyle S,} then p K | S = p K S , {\textstyle p_{K}{\big \vert }_{S}=p_{K\cap S},} where p K | S {\textstyle p_{K}{\big \vert }_{S}} denotes the restriction of p K {\textstyle p_{K}} to S . {\textstyle S.}

Examples

  1. If L {\textstyle {\mathcal {L}}} is a non-empty collection of subsets of X {\textstyle X} then p L ( x ) = inf { p L ( x ) : L L } {\textstyle p_{\cup {\mathcal {L}}}(x)=\inf \left\{p_{L}(x):L\in {\mathcal {L}}\right\}} for all x X , {\textstyle x\in X,} where L   = def   L L L . {\textstyle \cup {\mathcal {L}}~{\stackrel {\scriptscriptstyle {\text{def}}}{=}}~{\textstyle \bigcup \limits _{L\in {\mathcal {L}}}}L.}
    • Thus p K L ( x ) = min { p K ( x ) , p L ( x ) } {\textstyle p_{K\cup L}(x)=\min \left\{p_{K}(x),p_{L}(x)\right\}} for all x X . {\textstyle x\in X.}
  2. If L {\textstyle {\mathcal {L}}} is a non-empty collection of subsets of X {\textstyle X} and I X {\textstyle I\subseteq X} satisfies

{ x X : p L ( x ) < 1  for all  L L } I { x X : p L ( x ) 1  for all  L L } {\displaystyle \left\{x\in X:p_{L}(x)<1{\text{ for all }}L\in {\mathcal {L}}\right\}\quad \subseteq \quad I\quad \subseteq \quad \left\{x\in X:p_{L}(x)\leq 1{\text{ for all }}L\in {\mathcal {L}}\right\}} then p I ( x ) = sup { p L ( x ) : L L } {\textstyle p_{I}(x)=\sup \left\{p_{L}(x):L\in {\mathcal {L}}\right\}} for all x X . {\textstyle x\in X.}

The following examples show that the containment ( 0 , R ] K e > 0 ( 0 , R + e ) K {\textstyle (0,R]K\;\subseteq \;{\textstyle \bigcap \limits _{e>0}}(0,R+e)K} could be proper.

Example: If R = 0 {\textstyle R=0} and K = X {\textstyle K=X} then ( 0 , R ] K = ( 0 , 0 ] X = X = {\textstyle (0,R]K=(0,0]X=\varnothing X=\varnothing } but e > 0 ( 0 , e ) K = e > 0 X = X , {\textstyle {\textstyle \bigcap \limits _{e>0}}(0,e)K={\textstyle \bigcap \limits _{e>0}}X=X,} which shows that its possible for ( 0 , R ] K {\textstyle (0,R]K} to be a proper subset of e > 0 ( 0 , R + e ) K {\textstyle {\textstyle \bigcap \limits _{e>0}}(0,R+e)K} when R = 0. {\textstyle R=0.} {\textstyle \blacksquare }

The next example shows that the containment can be proper when R = 1 ; {\textstyle R=1;} the example may be generalized to any real R > 0. {\textstyle R>0.} Assuming that [ 0 , 1 ] K K , {\textstyle [0,1]K\subseteq K,} the following example is representative of how it happens that x X {\textstyle x\in X} satisfies p K ( x ) = 1 {\textstyle p_{K}(x)=1} but x ( 0 , 1 ] K . {\textstyle x\not \in (0,1]K.}

Example: Let x X {\textstyle x\in X} be non-zero and let K = [ 0 , 1 ) x {\textstyle K=[0,1)x} so that [ 0 , 1 ] K = K {\textstyle [0,1]K=K} and x K . {\textstyle x\not \in K.} From x ( 0 , 1 ) K = K {\textstyle x\not \in (0,1)K=K} it follows that p K ( x ) 1. {\textstyle p_{K}(x)\geq 1.} That p K ( x ) 1 {\textstyle p_{K}(x)\leq 1} follows from observing that for every e > 0 , {\textstyle e>0,} ( 0 , 1 + e ) K = [ 0 , 1 + e ) ( [ 0 , 1 ) x ) = [ 0 , 1 + e ) x , {\textstyle (0,1+e)K=[0,1+e)([0,1)x)=[0,1+e)x,} which contains x . {\textstyle x.} Thus p K ( x ) = 1 {\textstyle p_{K}(x)=1} and x e > 0 ( 0 , 1 + e ) K . {\textstyle x\in {\textstyle \bigcap \limits _{e>0}}(0,1+e)K.} However, ( 0 , 1 ] K = ( 0 , 1 ] ( [ 0 , 1 ) x ) = [ 0 , 1 ) x = K {\textstyle (0,1]K=(0,1]([0,1)x)=[0,1)x=K} so that x ( 0 , 1 ] K , {\textstyle x\not \in (0,1]K,} as desired. {\textstyle \blacksquare }

Positive homogeneity characterizes Minkowski functionals

The next theorem shows that Minkowski functionals are exactly those functions f : X [ 0 , ] {\textstyle f:X\to [0,\infty ]} that have a certain purely algebraic property that is commonly encountered.

TheoremLet f : X [ 0 , ] {\textstyle f:X\to [0,\infty ]} be any function. The following statements are equivalent:

  1. Strict positive homogeneity: f ( t x ) = t f ( x ) {\textstyle \;f(tx)=tf(x)} for all x X {\textstyle x\in X} and all positive real t > 0. {\textstyle t>0.}
    • This statement is equivalent to: f ( t x ) t f ( x ) {\textstyle f(tx)\leq tf(x)} for all x X {\textstyle x\in X} and all positive real t > 0. {\textstyle t>0.}
  2. f {\textstyle f} is a Minkowski functional: meaning that there exists a subset S X {\textstyle S\subseteq X} such that f = p S . {\textstyle f=p_{S}.}
  3. f = p K {\textstyle f=p_{K}} where K := { x X : f ( x ) 1 } . {\textstyle K:=\{x\in X:f(x)\leq 1\}.}
  4. f = p V {\textstyle f=p_{V}\,} where V := { x X : f ( x ) < 1 } . {\textstyle V\,:=\{x\in X:f(x)<1\}.}

Moreover, if f {\textstyle f} never takes on the value {\textstyle \,\infty \,} (so that the product 0 f ( x ) {\textstyle 0\cdot f(x)} is always well-defined) then this list may be extended to include:

  1. Positive/Nonnegative homogeneity: f ( t x ) = t f ( x ) {\textstyle f(tx)=tf(x)} for all x X {\textstyle x\in X} and all nonnegative real t 0 {\textstyle t\geq 0} .

This theorem can be extended to characterize certain classes of [ , ] {\textstyle [-\infty ,\infty ]} -valued maps (for example, real-valued sublinear functions) in terms of Minkowski functionals. For instance, it can be used to describe how every real homogeneous function f : X R {\textstyle f:X\to \mathbb {R} } (such as linear functionals) can be written in terms of a unique Minkowski functional having a certain property.

Characterizing Minkowski functionals on star sets

Proposition10Let f : X [ 0 , ] {\textstyle f:X\to [0,\infty ]} be any function and K X {\textstyle K\subseteq X} be any subset. The following statements are equivalent:

  1. f {\textstyle f} is (strictly) positive homogeneous, f ( 0 ) = 0 , {\textstyle f(0)=0,} and

    { x X : f ( x ) < 1 } K { x X : f ( x ) 1 } . {\displaystyle \{x\in X:f(x)<1\}\;\subseteq \;K\;\subseteq \;\{x\in X:f(x)\leq 1\}.}

  2. f {\textstyle f} is the Minkowski functional of K {\textstyle K} (that is, f = p K {\textstyle f=p_{K}} ), K {\textstyle K} contains the origin, and K {\textstyle K} is star-shaped at the origin.
    • The set K {\textstyle K} is star-shaped at the origin if and only if t k K {\textstyle tk\in K} whenever k K {\textstyle k\in K} and 0 t 1. {\textstyle 0\leq t\leq 1.} A set that is star-shaped at the origin is sometimes called a star set.9

Characterizing Minkowski functionals that are seminorms

In this next theorem, which follows immediately from the statements above, K {\textstyle K} is not assumed to be absorbing in X {\textstyle X} and instead, it is deduced that ( 0 , 1 ) K {\textstyle (0,1)K} is absorbing when p K {\textstyle p_{K}} is a seminorm. It is also not assumed that K {\textstyle K} is balanced (which is a property that K {\textstyle K} is often required to have); in its place is the weaker condition that ( 0 , 1 ) s K ( 0 , 1 ) K {\textstyle (0,1)sK\subseteq (0,1)K} for all scalars s {\textstyle s} satisfying | s | = 1. {\textstyle |s|=1.} The common requirement that K {\textstyle K} be convex is also weakened to only requiring that ( 0 , 1 ) K {\textstyle (0,1)K} be convex.

TheoremLet K {\textstyle K} be a subset of a real or complex vector space X . {\textstyle X.} Then p K {\textstyle p_{K}} is a seminorm on X {\textstyle X} if and only if all of the following conditions hold:

  1. ( 0 , ) K = X {\textstyle (0,\infty )K=X} (or equivalently, p K {\textstyle p_{K}} is real-valued).
  2. ( 0 , 1 ) K {\textstyle (0,1)K} is convex (or equivalently, p K {\textstyle p_{K}} is subadditive).
    • It suffices (but is not necessary) for K {\textstyle K} to be convex.
  3. ( 0 , 1 ) u K ( 0 , 1 ) K {\textstyle (0,1)uK\subseteq (0,1)K} for all unit scalars u . {\textstyle u.}
    • This condition is satisfied if K {\textstyle K} is balanced or more generally if u K K {\textstyle uK\subseteq K} for all unit scalars u . {\textstyle u.}

in which case 0 K {\textstyle 0\in K} and both ( 0 , 1 ) K = { x X : p ( x ) < 1 } {\textstyle (0,1)K=\{x\in X:p(x)<1\}} and e > 0 ( 0 , 1 + e ) K = { x X : p K ( x ) 1 } {\textstyle \bigcap _{e>0}(0,1+e)K=\left\{x\in X:p_{K}(x)\leq 1\right\}} will be convex, balanced, and absorbing subsets of X . {\textstyle X.}

Conversely, if f {\textstyle f} is a seminorm on X {\textstyle X} then the set V := { x X : f ( x ) < 1 } {\textstyle V:=\{x\in X:f(x)<1\}} satisfies all three of the above conditions (and thus also the conclusions) and also f = p V ; {\textstyle f=p_{V};} moreover, V {\textstyle V} is necessarily convex, balanced, absorbing, and satisfies ( 0 , 1 ) V = V = [ 0 , 1 ] V . {\textstyle (0,1)V=V=[0,1]V.}

CorollaryIf K {\textstyle K} is a convex, balanced, and absorbing subset of a real or complex vector space X , {\textstyle X,} then p K {\textstyle p_{K}} is a seminorm on X . {\textstyle X.}

Positive sublinear functions and Minkowski functionals

It may be shown that a real-valued subadditive function f : X R {\textstyle f:X\to \mathbb {R} } on an arbitrary topological vector space X {\textstyle X} is continuous at the origin if and only if it is uniformly continuous, where if in addition f {\textstyle f} is nonnegative, then f {\textstyle f} is continuous if and only if V := { x X : f ( x ) < 1 } {\textstyle V:=\{x\in X:f(x)<1\}} is an open neighborhood in X . {\textstyle X.} 11 If f : X R {\textstyle f:X\to \mathbb {R} } is subadditive and satisfies f ( 0 ) = 0 , {\textstyle f(0)=0,} then f {\textstyle f} is continuous if and only if its absolute value | f | : X [ 0 , ) {\textstyle |f|:X\to [0,\infty )} is continuous.

A nonnegative sublinear function is a nonnegative homogeneous function f : X [ 0 , ) {\textstyle f:X\to [0,\infty )} that satisfies the triangle inequality. It follows immediately from the results below that for such a function f , {\textstyle f,} if V := { x X : f ( x ) < 1 } {\textstyle V:=\{x\in X:f(x)<1\}} then f = p V . {\textstyle f=p_{V}.} Given K X , {\textstyle K\subseteq X,} the Minkowski functional p K {\textstyle p_{K}} is a sublinear function if and only if it is real-valued and subadditive, which is happens if and only if ( 0 , ) K = X {\textstyle (0,\infty )K=X} and ( 0 , 1 ) K {\textstyle (0,1)K} is convex.

Correspondence between open convex sets and positive continuous sublinear functions

Theorem11Suppose that X {\textstyle X} is a topological vector space (not necessarily locally convex or Hausdorff) over the real or complex numbers. Then the non-empty open convex subsets of X {\textstyle X} are exactly those sets that are of the form z + { x X : p ( x ) < 1 } = { x X : p ( x z ) < 1 } {\textstyle z+\{x\in X:p(x)<1\}=\{x\in X:p(x-z)<1\}} for some z X {\textstyle z\in X} and some positive continuous sublinear function p {\textstyle p} on X . {\textstyle X.}

See also

See also

Notes

Notes

  1. It is in general false that x r D {\textstyle x\in rD} if and only if p D ( x ) = r {\textstyle p_{D}(x)=r} (for example, consider when p K {\textstyle p_{K}} is a norm or a seminorm). The correct statement is: If 0 < r < {\textstyle 0<r<\infty } then x r D {\textstyle x\in rD} if and only if p D ( x ) = r {\textstyle p_{D}(x)=r} or p D ( x ) = 0. {\textstyle p_{D}(x)=0.}
  2. u {\textstyle u} is having unit length means that | u | = 1. {\textstyle |u|=1.}
  3. The map p K {\textstyle p_{K}} is called absolutely homogeneous if | s | p K ( x ) {\textstyle |s|p_{K}(x)} is well-defined and p K ( s x ) = | s | p K ( x ) {\textstyle p_{K}(sx)=|s|p_{K}(x)} for all x X {\textstyle x\in X} and all scalars s {\textstyle s} (not just non-zero scalars).
References

References

  1. Narici & Beckenstein 2011, p. 109.
  2. Narici & Beckenstein 2011, p. 119.
  3. Jarchow 1981, pp. 104–108.
  4. Narici & Beckenstein 2011, pp. 115–154.
  5. Schaefer 1999, p. 40.
  6. Narici & Beckenstein 2011, p. 119-120.
  7. Kubrusly 2011, p. 200.
  8. Schechter 1996, p. 316.
  9. Schechter 1996, p. 303.
  10. Schechter 1996, pp. 313–317.
  11. Narici & Beckenstein 2011, pp. 192–193.
Further reading

Further reading

  • F. Simeski, A. M. P. Boelens, and M. Ihme. "Modeling Adsorption in Silica Pores via Minkowski Functionals and Molecular Electrostatic Moments". Energies 13 (22) 5976 (2020). doi:10.3390/en13225976.