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 ${\displaystyle K}$ is a subset of a real or complex vector space ${\displaystyle X,}$ then the Minkowski functional or gauge of ${\displaystyle K}$ is defined to be the function ${\displaystyle p_{K}:X\to [0,\infty ],}$ valued in the extended real numbers, defined by ${\displaystyle p_{K}(x):=\inf\{r\in \mathbb {R} :r>0{\text{ and ))x\in rK\}\quad {\text{ for every ))x\in X,}$ where the infimum of the empty set is defined to be positive infinity ${\displaystyle \,\infty \,}$ (which is not a real number so that ${\displaystyle p_{K}(x)}$ would then not be real-valued).

The set ${\displaystyle K}$ is often assumed/picked to have properties, such as being an absorbing disk in ${\displaystyle X,}$ that guarantee that ${\displaystyle p_{K))$ will be a real-valued seminorm on ${\displaystyle X.}$ In fact, every seminorm ${\displaystyle p}$ on ${\displaystyle X}$ is equal to the Minkowski functional (that is, ${\displaystyle p=p_{K))$) of any subset ${\displaystyle K}$ of ${\displaystyle X}$ satisfying ${\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 ${\displaystyle X}$ 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 ${\displaystyle X}$ into certain algebraic properties of a function on ${\displaystyle X.}$

The Minkowski function is always non-negative (meaning ${\displaystyle p_{K}\geq 0}$). 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, ${\displaystyle p_{K))$ might not be real-valued since for any given ${\displaystyle x\in X,}$ the value ${\displaystyle p_{K}(x)}$ is a real number if and only if ${\displaystyle \{r>0:x\in rK\))$ is not empty. Consequently, ${\displaystyle K}$ is usually assumed to have properties (such as being absorbing in ${\displaystyle X,}$ for instance) that will guarantee that ${\displaystyle p_{K))$ is real-valued.

## Definition

Let ${\displaystyle K}$ be a subset of a real or complex vector space ${\displaystyle X.}$ Define the gauge of ${\displaystyle K}$ or the Minkowski functional associated with or induced by ${\displaystyle K}$ as being the function ${\displaystyle p_{K}:X\to [0,\infty ],}$ valued in the extended real numbers, defined by ${\displaystyle p_{K}(x):=\inf\{r>0:x\in rK\},}$ where recall that the infimum of the empty set is ${\displaystyle \,\infty \,}$ (that is, ${\displaystyle \inf \varnothing =\infty }$). Here, ${\displaystyle \{r>0:x\in rK\))$ is shorthand for ${\displaystyle \{r\in \mathbb {R} :r>0{\text{ and ))x\in rK\}.}$

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

Some conditions making a gauge real-valued

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

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

## Motivating examples

Example 1

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

Example 2

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

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

Therefore, ${\displaystyle p_{K))$ is a seminorm on ${\displaystyle X,}$ 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, ${\displaystyle p_{K}(x)=0}$ need not imply ${\displaystyle x=0.}$ In the above example, one can take a nonzero ${\displaystyle x}$ from the kernel of ${\displaystyle f.}$ Consequently, the resulting topology need not be Hausdorff.

## Common conditions guaranteeing gauges are seminorms

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

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

Theorem[2] — If ${\displaystyle K}$ is an absorbing disk in a vector space ${\displaystyle X}$ then the Minkowski functional of ${\displaystyle K,}$ which is the map ${\displaystyle p_{K}:X\to [0,\infty )}$ defined by ${\displaystyle p_{K}(x):=\inf\{r>0:x\in rK\},}$ is a seminorm on ${\displaystyle X.}$ Moreover, ${\displaystyle p_{K}(x)={\frac {1}{\sup\{r>0:rx\in K\))}.}$

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

### Gauges of absorbing disks

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

Theorem — Assume that ${\displaystyle K}$ is an absorbing subset of ${\displaystyle X.}$ It is shown that:

1. If ${\displaystyle K}$ is convex then ${\displaystyle p_{K))$ is subadditive.
2. If ${\displaystyle K}$ is balanced then ${\displaystyle p_{K))$ is absolutely homogeneous; that is, ${\displaystyle p_{K}(sx)=|s|p_{K}(x)}$ for all scalars ${\displaystyle s.}$
Proof that the Gauge of an absorbing disk is a seminorm

A simple geometric argument that shows convexity of ${\displaystyle K}$ implies subadditivity is as follows. Suppose for the moment that ${\displaystyle p_{K}(x)=p_{K}(y)=r.}$ Then for all ${\displaystyle e>0,}$ ${\displaystyle x,y\in K_{e}:=(r,e)K.}$ Since ${\displaystyle K}$ is convex and ${\displaystyle r+e\neq 0,}$ ${\displaystyle K_{e))$ is also convex. Therefore, ${\displaystyle {\frac {1}{2))x+{\frac {1}{2))y\in K_{e}.}$ By definition of the Minkowski functional ${\displaystyle p_{K},}$ ${\displaystyle p_{K}\left({\frac {1}{2))x+{\frac {1}{2))y\right)\leq r+e={\frac {1}{2))p_{K}(x)+{\frac {1}{2))p_{K}(y)+e.}$

But the left hand side is ${\displaystyle {\frac {1}{2))p_{K}(x+y),}$ so that ${\displaystyle p_{K}(x+y)\leq p_{K}(x)+p_{K}(y)+2e.}$

Since ${\displaystyle e>0}$ was arbitrary, it follows that ${\displaystyle p_{K}(x+y)\leq p_{K}(x)+p_{K}(y),}$ which is the desired inequality. The general case ${\displaystyle p_{K}(x)>p_{K}(y)}$ is obtained after the obvious modification.

Convexity of ${\displaystyle K,}$ together with the initial assumption that the set ${\displaystyle \{r>0:x\in rK\))$ is nonempty, implies that ${\displaystyle K}$ is absorbing.

Balancedness and absolute homogeneity

Notice that ${\displaystyle K}$ being balanced implies that ${\displaystyle \lambda x\in rK\quad {\mbox{if and only if))\quad x\in {\frac {r}{|\lambda |))K.}$

Therefore ${\displaystyle p_{K}(\lambda x)=\inf \left\{r>0:\lambda x\in rK\right\}=\inf \left\{r>0:x\in {\frac {r}{|\lambda |))K\right\}=\inf \left\{|\lambda |{\frac {r}{|\lambda |))>0:x\in {\frac {r}{|\lambda |))K\right\}=|\lambda |p_{K}(x).}$

### Algebraic properties

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

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

### Topological properties

Assume that ${\displaystyle X}$ is a (real or complex) topological vector space (TVS) (not necessarily Hausdorff or locally convex) and let ${\displaystyle K}$ be an absorbing disk in ${\displaystyle X.}$ Then ${\displaystyle \operatorname {Int} _{X}K\;\subseteq \;\{x\in X:p_{K}(x)<1\}\;\subseteq \;K\;\subseteq \;\{x\in X:p_{K}(x)\leq 1\}\;\subseteq \;\operatorname {Cl} _{X}K,}$ where ${\displaystyle \operatorname {Int} _{X}K}$ is the topological interior and ${\displaystyle \operatorname {Cl} _{X}K}$ is the topological closure of ${\displaystyle K}$ in ${\displaystyle X.}$[6] Importantly, it was not assumed that ${\displaystyle p_{K))$ was continuous nor was it assumed that ${\displaystyle K}$ had any topological properties.

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

## Minimal requirements on the set

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

### Properties

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

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

Summary — Suppose that ${\displaystyle K}$ is a subset of a real or complex vector space ${\displaystyle X.}$

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

The proofs of these basic properties are straightforward exercises so only the proofs of the most important statements are given.

The proof that a convex subset ${\displaystyle A\subseteq X}$ that satisfies ${\displaystyle (0,\infty )A=X}$ is necessarily absorbing in ${\displaystyle X}$ is straightforward and can be found in the article on absorbing sets.

For any real ${\displaystyle t>0,}$ ${\displaystyle \{r>0:tx\in rK\}=\{t(r/t):x\in (r/t)K\}=t\{s>0:x\in sK\))$ so that taking the infimum of both sides shows that ${\displaystyle p_{K}(tx)=\inf\{r>0:tx\in rK\}=t\inf\{s>0:x\in sK\}=tp_{K}(x).}$ This proves that Minkowski functionals are strictly positive homogeneous. For ${\displaystyle 0\cdot p_{K}(x)}$ to be well-defined, it is necessary and sufficient that ${\displaystyle p_{K}(x)\neq \infty ;}$ thus ${\displaystyle p_{K}(tx)=tp_{K}(x)}$ for all ${\displaystyle x\in X}$ and all non-negative real ${\displaystyle t\geq 0}$ if and only if ${\displaystyle p_{K))$ is real-valued.

The hypothesis of statement (7) allows us to conclude that ${\displaystyle p_{K}(sx)=p_{K}(x)}$ for all ${\displaystyle x\in X}$ and all scalars ${\displaystyle s}$ satisfying ${\displaystyle |s|=1.}$ Every scalar ${\displaystyle s}$ is of the form ${\displaystyle re^{it))$ for some real ${\displaystyle t}$ where ${\displaystyle r:=|s|\geq 0}$ and ${\displaystyle e^{it))$ is real if and only if ${\displaystyle s}$ is real. The results in the statement about absolute homogeneity follow immediately from the aforementioned conclusion, from the strict positive homogeneity of ${\displaystyle p_{K},}$ and from the positive homogeneity of ${\displaystyle p_{K))$ when ${\displaystyle p_{K))$ is real-valued. ${\displaystyle \blacksquare }$

### Examples

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

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

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

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

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

### Positive homogeneity characterizes Minkowski functionals

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

Theorem — Let ${\displaystyle f:X\to [0,\infty ]}$ be any function. The following statements are equivalent:

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

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

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

If ${\displaystyle f(tx)\leq tf(x)}$ holds for all ${\displaystyle x\in X}$ and real ${\displaystyle t>0}$ then ${\displaystyle tf(x)=tf\left({\tfrac {1}{t))(tx)\right)\leq t{\tfrac {1}{t))f(tx)=f(tx)\leq tf(x)}$ so that ${\displaystyle tf(x)=f(tx).}$

Only (1) implies (3) will be proven because afterwards, the rest of the theorem follows immediately from the basic properties of Minkowski functionals described earlier; properties that will henceforth be used without comment. So assume that ${\displaystyle f:X\to [0,\infty ]}$ is a function such that ${\displaystyle f(tx)=tf(x)}$ for all ${\displaystyle x\in X}$ and all real ${\displaystyle t>0}$ and let ${\displaystyle K:=\{y\in X:f(y)\leq 1\}.}$

For all real ${\displaystyle t>0,}$ ${\displaystyle f(0)=f(t0)=tf(0)}$ so by taking ${\displaystyle t=2}$ for instance, it follows that either ${\displaystyle f(0)=0}$ or ${\displaystyle f(0)=\infty .}$ Let ${\displaystyle x\in X.}$ It remains to show that ${\displaystyle f(x)=p_{K}(x).}$

It will now be shown that if ${\displaystyle f(x)=0}$ or ${\displaystyle f(x)=\infty }$ then ${\displaystyle f(x)=p_{K}(x),}$ so that in particular, it will follow that ${\displaystyle f(0)=p_{K}(0).}$ So suppose that ${\displaystyle f(x)=0}$ or ${\displaystyle f(x)=\infty ;}$ in either case ${\displaystyle f(tx)=tf(x)=f(x)}$ for all real ${\displaystyle t>0.}$ Now if ${\displaystyle f(x)=0}$ then this implies that that ${\displaystyle tx\in K}$ for all real ${\displaystyle t>0}$ (since ${\displaystyle f(tx)=0\leq 1}$), which implies that ${\displaystyle p_{K}(x)=0,}$ as desired. Similarly, if ${\displaystyle f(x)=\infty }$ then ${\displaystyle tx\not \in K}$ for all real ${\displaystyle t>0,}$ which implies that ${\displaystyle p_{K}(x)=\infty ,}$ as desired. Thus, it will henceforth be assumed that ${\displaystyle R:=f(x)}$ a positive real number and that ${\displaystyle x\neq 0}$ (importantly, however, the possibility that ${\displaystyle p_{K}(x)}$ is ${\displaystyle 0}$ or ${\displaystyle \,\infty \,}$ has not yet been ruled out).

Recall that just like ${\displaystyle f,}$ the function ${\displaystyle p_{K))$ satisfies ${\displaystyle p_{K}(tx)=tp_{K}(x)}$ for all real ${\displaystyle t>0.}$ Since ${\displaystyle 0<{\tfrac {1}{R))<\infty ,}$ ${\displaystyle p_{K}(x)=R=f(x)}$ if and only if ${\displaystyle p_{K}\left({\tfrac {1}{R))x\right)=1=f\left({\tfrac {1}{R))x\right)}$ so assume without loss of generality that ${\displaystyle R=1}$ and it remains to show that ${\displaystyle p_{K}\left({\tfrac {1}{R))x\right)=1.}$ Since ${\displaystyle f(x)=1,}$ ${\displaystyle x\in K\subseteq (0,1]K,}$ which implies that ${\displaystyle p_{K}(x)\leq 1}$ (so in particular, ${\displaystyle p_{K}(x)\neq \infty }$ is guaranteed). It remains to show that ${\displaystyle p_{K}(x)\geq 1,}$ which recall happens if and only if ${\displaystyle x\not \in (0,1)K.}$ So assume for the sake of contradiction that ${\displaystyle x\in (0,1)K}$ and let ${\displaystyle 0 and ${\displaystyle k\in K}$ be such that ${\displaystyle x=rk,}$ where note that ${\displaystyle k\in K}$ implies that ${\displaystyle f(k)\leq 1.}$ Then ${\displaystyle 1=f(x)=f(rk)=rf(k)\leq r<1.}$ ${\displaystyle \blacksquare }$

This theorem can be extended to characterize certain classes of ${\displaystyle [-\infty ,\infty ]}$-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 ${\displaystyle f:X\to \mathbb {R} }$ (such as linear functionals) can be written in terms of a unique Minkowski functional having a certain property.

### Characterizing Minkowski functionals on star sets

Proposition[10] — Let ${\displaystyle f:X\to [0,\infty ]}$ be any function and ${\displaystyle K\subseteq X}$ be any subset. The following statements are equivalent:

1. ${\displaystyle f}$ is (strictly) positive homogeneous, ${\displaystyle f(0)=0,}$ and ${\displaystyle \{x\in X:f(x)<1\}\;\subseteq \;K\;\subseteq \;\{x\in X:f(x)\leq 1\}.}$
2. ${\displaystyle f}$ is the Minkowski functional of ${\displaystyle K}$ (that is, ${\displaystyle f=p_{K))$), ${\displaystyle K}$ contains the origin, and ${\displaystyle K}$ is star-shaped at the origin.
• The set ${\displaystyle K}$ is star-shaped at the origin if and only if ${\displaystyle tk\in K}$ whenever ${\displaystyle k\in K}$ and ${\displaystyle 0\leq t\leq 1.}$ 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, ${\displaystyle K}$ is not assumed to be absorbing in ${\displaystyle X}$ and instead, it is deduced that ${\displaystyle (0,1)K}$ is absorbing when ${\displaystyle p_{K))$ is a seminorm. It is also not assumed that ${\displaystyle K}$ is balanced (which is a property that ${\displaystyle K}$ is often required to have); in its place is the weaker condition that ${\displaystyle (0,1)sK\subseteq (0,1)K}$ for all scalars ${\displaystyle s}$ satisfying ${\displaystyle |s|=1.}$ The common requirement that ${\displaystyle K}$ be convex is also weakened to only requiring that ${\displaystyle (0,1)K}$ be convex.

Theorem — Let ${\displaystyle K}$ be a subset of a real or complex vector space ${\displaystyle X.}$ Then ${\displaystyle p_{K))$ is a seminorm on ${\displaystyle X}$ if and only if all of the following conditions hold:

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

in which case ${\displaystyle 0\in K}$ and both ${\displaystyle (0,1)K=\{x\in X:p(x)<1\))$ and ${\displaystyle \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 ${\displaystyle X.}$

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

Corollary — If ${\displaystyle K}$ is a convex, balanced, and absorbing subset of a real or complex vector space ${\displaystyle X,}$ then ${\displaystyle p_{K))$ is a seminorm on ${\displaystyle X.}$

### Positive sublinear functions and Minkowski functionals

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

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

Correspondence between open convex sets and positive continuous sublinear functions

Theorem[11] — Suppose that ${\displaystyle X}$ 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 ${\displaystyle X}$ are exactly those sets that are of the form ${\displaystyle z+\{x\in X:p(x)<1\}=\{x\in X:p(x-z)<1\))$ for some ${\displaystyle z\in X}$ and some positive continuous sublinear function ${\displaystyle p}$ on ${\displaystyle X.}$

Proof

Let ${\displaystyle V\neq \varnothing }$ be an open convex subset of ${\displaystyle X.}$ If ${\displaystyle 0\in V}$ then let ${\displaystyle z:=0}$ and otherwise let ${\displaystyle z\in V}$ be arbitrary. Let ${\displaystyle p=p_{K}:X\to [0,\infty )}$ be the Minkowski functional of ${\displaystyle K:=V-z}$ where this convex open neighborhood of the origin satisfies ${\displaystyle (0,1)K=K.}$ Then ${\displaystyle p}$ is a continuous sublinear function on ${\displaystyle X}$ since ${\displaystyle V-z}$ is convex, absorbing, and open (however, ${\displaystyle p}$ is not necessarily a seminorm since it is not necessarily absolutely homogeneous). From the properties of Minkowski functionals, we have ${\displaystyle p_{K}^{-1}([0,1))=(0,1)K,}$ from which it follows that ${\displaystyle V-z=\{x\in X:p(x)<1\))$ and so ${\displaystyle V=z+\{x\in X:p(x)<1\}.}$ Since ${\displaystyle z+\{x\in X:p(x)<1\}=\{x\in X:p(x-z)<1\},}$ this completes the proof. ${\displaystyle \blacksquare }$

## Notes

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

## References

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