Mapping radii of metric spaces∗
George M. Bergman
November 4, 2018
Dedicated to the memory of David Gale
Abstract
It is known that every closed curve of length ≤ 4 in Rn (n > 0) can be surrounded by a sphere
of radius 1, and that this is the best bound. Letting S denote the circle of circumference 4, with the
arc-length metric, we here express this fact by saying that the mapping radius of S in Rn is 1.
Tools are developed for estimating the mapping radius of a metric space X in a metric space Y. In
particular, it is shown that for X a bounded metric space, the supremum of the mapping radii of X in
all convex subsets of normed metric spaces is equal to the infimum of the sup norms of all convex linear
combinations of the functions d(x,−) : X → R (x ∈ X).
Several explicit mapping radii are calculated, and open questions noted.
1 The definition, and three examples.
Definition 1. We will denote by Metr the category whose objects are metric spaces, and whose morphisms
are nonexpansive maps. That is, for metric spaces X and Y we let
(1) Metr(X,Y ) = {f : X → Y | (∀x0, x1 ∈ X) d(f(x0), f(x1)) ≤ d(x0, x1)}.
Throughout this note, a map of metric spaces will mean a morphism in Metr.
Given a nonempty subset A of a metric space Y, we define its radius by
(2) radY (A) = infy∈Y supa∈A d(a, y),
a nonnegative real number or +∞. For metric spaces X and Y, we define the mapping radius of X in Y
(3) map-rad(X,Y ) = supf∈Metr(X,Y ) radY (f(X))
= supf∈Metr(X,Y ) infy∈Y supx∈X d(f(x), y).
If X is a metric space and Y a class of metric spaces, we likewise define
(4) map-rad(X,Y) = supY ∈Y map-rad(X,Y )
= supY ∈Y, f∈Metr(X,Y ) infy∈Y supx∈X d(f(x), y).
(The term “mapping radius” occurs occasionally in complex analysis with an unrelated meaning [15, Def. 7.11].)
All vector spaces in this note will be over the field of real numbers unless the contrary is stated.
The result stated in the first sentence of the abstract has been discovered many times [5], [6], [18], [19],
[25]. (Usually, the length of the closed curve is given as 1 and the radius of the sphere as 1/4, but the
scaled-up version will be more convenient here.) Let us obtain it in somewhat greater generality.
∗2000 Mathematics Subject Classifications. Primary: 54E40. Secondary: 46B20, 46E15, 52A40.
Keywords: nonexpansive map between metric spaces, maximum radius of image, convex subset of a normed vector space.
Any updates, errata, related references etc., learned of after publication will be noted at
http://math.berkeley.edu/∼ gbergman/papers/ .
http://arxiv.org/abs/0704.0275v2
http://math.berkeley.edu/\protect 
Lemma 2. Let S denote the circle of circumference 4, with the arc-length metric. Then for any nonzero
normed vector space V, we have map-rad(S, V ) = 1.
Proof. In V, any 1-dimensional subspace U is isometric to the real line R, and we can map S into R, and
hence into U, by “folding it flat”, getting for image an interval of length 2. Since this interval has points at
distance 2 apart, its radius in V cannot be less than 1, so map-rad(S, V ) ≥ 1.
For the reverse inequality, consider any map f : S → V. We wish to find a point y ∈ V having distance
≤ 1 from every point of f(S). Let p and q be any two antipodal points of S, and let
y = (f(p) + f(q))/2.
Every point x ∈ S lies on a length-2 arc between p and q in S, hence d(p, x) + d(q, x) = 2, hence
d(f(p), f(x))+d(f(q), f(x)) ≤ 2, i.e., (d(f(p), f(x))+d(f(q), f(x)))/2 ≤ 1, so d((f(p)+f(q))/2, f(x)) ≤ 1,
as claimed.
Let us make explicit the argument used at the very last step above. It is the c1 = c2 = 1/2 case of
(5) If c1, . . . , cn are nonnegative real numbers summing to 1, and v1, . . . , vn are elements of a
normed vector space V, then for all w ∈ V, d(
ci vi, w) ≤
ci d(vi, w).
This can be seen by writing the left-hand side as ||(
civi) − w|| = ||
ci (vi − w)|| ≤
||ci (vi − w)|| =
ci d(vi, w).
Consider next the union X of two circles S0 and S1, each of circumference 4, intersecting in a pair of
points antipodal in each (e.g., take for S0 and S1 any two distinct great circles on a sphere of circumfer-
ence 4), again with the arc-length metric. We can show that this X also has mapping radius ≤ 1 in V by
the same argument as before, except that where we previously used an arbitrary pair of antipodal points,
we are now forced to use precisely the pair at which our circles intersect. We are not so restricted in the
example showing that radius 1 can actually be achieved – we can stretch one circle taut between any two
antipodal points, and for most choices of those points, we have a great deal of freedom as to what to do with
the other circle. In any case, we have
Lemma 3. Let X be the union of two circles S0 and S1, each of circumference 4, intersecting in a pair
of points antipodal in each, with the arc-length metric. Then for any nonzero normed vector space V, we
have map-rad(X,V ) = 1.
We could apply the same method to any number of circles joined at a common pair of antipodal points; but
let us move in a different direction. Again picturing S0 and S1 as great circles on a sphere of circumference 4
in Euclidean 3-space, assume they meet at right angles, and call their points of intersection the north and
south poles. Let us bring in a third circle, S2, the equator, and let X = S0 ∪ S1 ∪ S2, again with the arc
length metric.
We no longer have a pair of antipodal points belonging to all three circles; rather, we have three pairs of
points, S1 ∩ S2 = {p0, q0}, S2 ∩ S0 = {p1, q1}, and S0 ∩ S1 = {p2, q2}. Now given a normed vector space
V and a map f : X → V in Metr, suppose we let
(6) y = (f(p0) + f(q0) + f(p1) + f(q1) + f(p2) + f(q2))/6.
What can we conclude about d(y, f(x)) for x ∈ X ?
Say x ∈ S2. Since both {p0, q0}, and {p1, q1} are pairs of antipodal points of S2, we have d(p0, x) +
d(q0, x) = d(p1, x) + d(q1, x) = 2. The same will not be true of d(p2, x) and d(q2, x). To determine how
large these can get, let us take x ∈ S2 as far as possible (under our arclength metric) from the intersections
of S2 with our two circles through the poles p2 and q2. This happens when x is at the midpoint of any
of the quadrants into which p0, q0, p1 and q1 divide S2; in this situation, d(p2, x) = d(q2, x) = 3/2.
(Each quadrant has arc-length 1, and one has to go a quadrant and a half to get from p2 or q2 to x.) We
see, in fact, that for any x ∈ S2 we have d(p2, x) = d(q2, x) ≤ 3/2, hence d(p2, x) + d(q2, x) ≤ 3. Now
applying any map f : X → V, and invoking (5) with all ci = 1/6, we see that for y as in (6) we have
d(y, f(x)) ≤ (2 + 2 + 3)/6 = 7/6. We have proved this for x ∈ S2; by symmetry, it is also true for x lying
on S0 or S1. This allows us to conclude, not that map-rad(X,V ) = 1 as in the preceding two cases, but
(7) map-rad(X,V ) ≤ 7/6.
And in fact, there do exist maps f : X → V with radV (f(X)) > 1. To describe such a map, note that
X can be identified with the 1-skeleton of a regular octahedron of edge 1. In the next few paragraphs, let
us put aside our picture of X in terms of great circles on a sphere, and replace it with this (straight-edged)
octahedral skeleton.
If we look at our octahedron in Euclidean 3-space from a direction perpendicular to one of its faces, we see
that face and the opposite one as overlapping, oppositely oriented equilateral triangles, with vertices joined
by the remaining 6 edges, which look like a regular hexagon. Now suppose we regard these two opposite
triangular faces as made of stiff wire, and the other 6 edges as made of string. Then if we bring the planes of
the two wire triangles closer to one another, the string edges will loosen. Suppose, however, that we rotate
the top triangle clockwise as they approach one another, so that three of those strings are kept taut, while
the other three become still looser. When the planes of our wire triangles meet, those wire triangles will
coincide, and the three taut string edges will fall together with the three edges of that triangle, while the
three loose ones become loops, hanging from the three vertices. Let us lock the two wire triangles together,
and pull the three loops taut, radially away from the center of symmetry of the triangle.
What we then have is the image of a certain map f in Metr from our octahedral skeleton X into a
plane, which we can identify with R2. We see that radR2(f(X)) will be the distance from the center of
symmetry of our figure to each of the three points to which the drawn-out loops are stretched; i.e., the sum of
the distance from the center of symmetry to each vertex of the triangle, and the length of the stretched loop
attached thereto. The former distance is two thirds of the altitude of the triangle, (2/3)(
3/2) = 1/
and the latter length is 1/2 (since the loop doubles back), so
(8) radR2(f(X)) = 1/
3 + 1/2. Hence, map-rad(X,R2) ≥ 1/
3 + 1/2 > 1.
This shows that our three-circle space does indeed behave differently from the preceding one- and two-
circle examples.
However 1/
3 + 1/2 ≈ 1.0773, which falls well short of the upper bound 7/6 ≈ 1.1667 of (7).
We can overcome this deficiency by using a different norm on R2. Let V be R2 with the norm whose
unit disc is the region enclosed by a regular hexagon H of unit side. Note that the 6 sides of H are parallel
to the 6 radii joining 0 to the vertices of H, hence these sides have length 1 in the new metric, just as
in the Euclidean metric, and indeed, any line segment in one of those directions will have the same length
in both metrics. Now let us map X, still pictured as the 1-skeleton of a regular octahedron of side 1 in
Euclidean 3-space, into V so that, as before, two opposite triangles are embedded isometrically (now under
the metric of V ), and made to fall together with each other and with three of the other edges, while the
remaining three edges form loops that are stretched radially outward as far as they will go. Let us moreover
take the sides of our image-triangle to be parallel to three sides of H.
The map X → R2 that does this is almost the same one as before. The 9 edges that end up parallel to
edges of H are mapped exactly as before, since distances in those directions are the same in the two metrics.
The three folded loops end up set-theoretically smaller than before, since the new metric is greater in their
direction than is the Euclidean metric, and they go out a distance 1/2 in the new metric before turning back;
but they still contribute the value 1/2 to the calculation of the radius of our image of X. The significant
change in that calculation concerns the distance from the center of our triangle to its three vertices. Looking
at our triangle as a translate of one of the 6 equilateral triangles into which H is decomposed by its radii,
we see that the altitude of that triangle is equal to its side in this metric (since the midpoint of a side of H
has the same distance, 1, from the origin as a vertex of H does). Hence the distance from the center to a
vertex is 2/3. Adding to this the distance 1/2 from that vertex to the end of the loop attached to it, we
get 2/3 + 1/2 = 7/6. Assuming that the center of our triangle is indeed the minimizing point defining the
radius (i.e., is a value of y that yields the infimum (2); we will verify this in Lemma 5), this achieves the
upper bound (7). Summarizing, and making a few supplementary observations, we have
Lemma 4. Let X be the 1-skeleton of a regular octahedron of side 1, under the arc-length metric. Then
for any nonzero normed vector space V,
(9) 1 ≤ map-rad(X,V ) ≤ 7/6.
The exact value of map-rad(X,V ) is 1 if V is 1-dimensional, is ≥ 1/
3 + 1/2 if V is Rn (n ≥ 2)
under the Euclidean norm, and is 7/6 if V is R2 under the norm having for unit circle a regular hexagon.
Proof. The lower bound 1 in (9) is gotten as in the last full sentence before Lemma 3, by regarding X
as S0 ∪ S1 ∪ S2, straightening out one of these circles to cover a segment of length 2 in a 1-dimensional
subspace of V, and letting the other two circles collapse into that line in any way. (Or for a construction
that relies less on geometric intuition, pick any p ∈ S0, map X into R by the function d(p,−), note that
this map sends p and the point antipodal to p on S0 to 0 and 2, respectively, and embed R in V.) As
before, such an image of X has points 2 units apart, and so has radius ≥ 1 in V by the triangle inequality.
The upper bound 7/6 was obtained in (7).
To see that when V itself is 1-dimensional, the value 1 is not exceeded, note that the distance between
any two points of X is ≤ 2. Hence the image of X under any map into such a V is a segment of length
≤ 2, hence of radius ≤ 1.
The lower bounds 1/
3 + 1/2 and 7/6 for V = R2 with the two indicated norms were obtained above
by explicit mappings.
Let us now justify the assumption we made just before the statement of the above lemma, about the
center from which we computed the radius.
Lemma 5. Let V be a normed vector space, A a nonempty subset of V, and G a finite group of isometries
of V which preserve A. Then
(10) radV (A) = infy∈V G supa∈A d(a, y),
where V G is the fixed-point set of G.
In particular, if V G is a singleton {v0}, then
(11) radV (A) = supa∈A d(a, v0).
Proof. Given v ∈ V, let
(12) y = |G|−1
and note that this point lies in V G, and that for any a ∈ A,
(13) d(a, y) = d(a, |G|−1
gv) ≤
|G|−1d(a, gv) = |G|−1
d(g−1a, v)
≤ |G|−1
supb∈A d(b, v) = supb∈A d(b, v).
Here the first inequality holds by (5) and the second by considering b = g−1a. Hence for y ∈ V G defined
by (12), supa∈A d(a, y) ≤ supa∈A d(a, v), from which (10) follows. The final assertion is a special case.
For V = R2 with a regular hexagon as unit circle, the group G generated by a rotation by 2π/3 about
any point is an isometry of V, and if we take that point to be the center of symmetry of the set f(X) we
were looking at above, G preserves f(X) and has that center of symmetry as unique fixed point; so the
above lemma justifies our description of the radius of f(X) in terms of distance from that point. In the
earlier computation using the Euclidean metric on R2, we “saw” that the radius was measured from the
center of symmetry; this is now likewise justified by Lemma 5.
Lemma 4 leaves open
Question 6. For V = R2 under the Euclidean norm, and X the 1-skeleton of a regular octahedron of
side 1, where does map-rad(X,V ) lie within [1/
3 + 1/2, 7/6] ?
For V = Rn, again with the Euclidean norm, but n > 2, is the answer the same?
Having whetted our appetite with this example, let us prove some general results.
2 General properties of mapping radii.
Lemma 7. Let X, X ′, Y, Y ′ be nonempty metric spaces, Y and Y′ classes of such metric spaces, and
V and V ′ normed vector spaces.
(i) If there exists a surjective map h : X → X ′ (or more generally, a map X → X ′ with dense image) in
Metr, then map-rad(X ′, Y ) ≤ map-rad(X,Y ).
(ii) If Y ′ ⊆ Y, then for any nonempty subset A of Y ′ we have radY ′(A) ≥ radY (A). Here equality will
hold if Y ′ is a retract of Y ; i.e., if the inclusion of Y ′ in Y has a left inverse in Metr.
Hence if Y ′ is a retract of Y, then map-rad(X,Y ′) ≤ map-rad(X,Y ). In particular, this is true if Y
is a normed vector space (or more generally, a convex subset of such a space) and Y ′ the fixed subspace
(respectively, subset) of a finite group G of affine isometries of Y.
(iii) If Y′ ⊆ Y, then map-rad(X,Y′) ≤ map-rad(X,Y).
(iv) In contrast to (i) and (ii), for X ′ ⊆ X, either of the numbers map-rad(X ′, Y ) and map-rad(X,Y )
can be greater than the other, and if Y ′ ⊆ Y, or if Y ′ is a surjective image of Y in Metr, either of the
numbers map-rad(X,Y ′) and map-rad(X,Y ) can be greater than the other.
Proof. (i) Suppose h : X → X ′ has dense image. Then for any f : X ′ → Y, fh(X) is dense in f(X ′),
hence radY (fh(X)) = radY (f(X
′)), so the terms of the supremum defining map-rad(X,Y ) include all the
terms of the supremum defining map-rad(X ′, Y ), from which the asserted inequality follows.
(ii) The terms of the infimum defining radY (A) include the terms of the infimum defining radY ′(A),
giving the first inequality.
If there exists a retraction e of Y onto Y ′, then for every y ∈ Y and a ∈ A we have d(a, e(y)) ≤ d(a, y),
since e is nonexpansive and fixes points of A. Hence supa∈A d(a, e(y)) ≤ supa∈A d(a, y), and taking the
infimum of this over y ∈ Y, we get radY ′(A) ≤ radY (A). This and the previous inequality give the asserted
equality. Since Metr(X,Y ′) ⊆Metr(X,Y ), we also get map-rad(X,Y ′) ≤ map-rad(X,Y ), as claimed.
If Y is a convex subset of a normed vector space, and Y ′ the fixed set of a finite group G as in the final
assertion, note that the function e(v) = |G|−1
gv used in the proof of Lemma 5 is nonexpansive:
d(e(v), e(w)) = ||e(v)− e(w)|| = ||e(v − w)|| ≤ ||v − w|| = d(v, w),
and is a retraction of Y onto Y G.
(iii) This is again a case of suprema of a smaller and a larger set of real numbers.
(iv) The assertion for X ′ ⊆ X can be seen from the following mapping radii, where subsets of R are
given the induced metric:
map-rad({0}, {0, 2}) = 0, map-rad({0, 2}, {0, 2}) = 2, map-rad({0, 1, 2}, {0, 2}) = 0.
The assertion for Y ′ ⊆ Y is shown by the observations
map-rad({0, 2}, {0}) = 0, map-rad({0, 2}, {0, 2}) = 2, map-rad({0, 2}, {0, 1, 2}) = 1.
Finally, to get the case where Y ′ is a surjective image of Y, note that we have surjections {0, 3} → {0, 2} →
{0, 1} in Metr, and that
map-rad({0, 2}, {0, 3}) = 0, map-rad({0, 2}, {0, 2}) = 2, map-rad({0, 2}, {0, 1}) = 1.
(With a bit more work, one can construct sets X, Y0, Y1, Y2 ⊆ R such that each Yi+1 is both a subset and
a surjective image of Yi, and such that map-rad(X,Y0) < map-rad(X,Y1) > map-rad(X,Y2).)
To state consequences of the above results, let us fix some notation.
Definition 8. For n ≥ 0, n-dimensional Euclidean space, i.e., Rn with the Euclidean norm, will be denoted
n. The class of all Euclidean spaces, {En | n ≥ 0}, will be denoted Euc.
The class of all normed vector spaces, regarded as metric spaces, will be denoted NmV. The class of all
convex subsets of normed vector spaces, regarded as metric spaces, will be denoted Conv.
The diameter of a metric space X will be defined by diam(X) = supx,y∈X d(x, y).
Corollary 9. If X is a nonempty metric space, then
(14) map-rad(X,E1) ≤ map-rad(X,E2) ≤ . . . ≤ map-rad(X,En) ≤ . . . ,
with supremum map-rad(X,Euc). Further,
(15) diam(X)/2 = map-rad(X, E1) ≤ map-rad(X, Euc) ≤ map-rad(X, NmV)
≤ map-rad(X, Conv) ≤ map-rad(X, Metr) = radX(X) ≤ diam(X).
Proof. Since En is the fixed subspace of a reflection of En+1, the final assertion of Lemma 7(ii) gives (14).
(We could have put “0 = map-rad(X, E0) ≤” at the left end of (14); but this would complicate some
references we will want to make to (14) later.) By definition, map-rad(X,Euc) is the supremum of these
values.
To see the initial equality of (15), note on the one hand that under any nonexpansive map f : X → E1,
the images of any two points of X are ≤ diam(X) apart, hence f(X) must lie in an interval of length
≤ diam(X), and any interval in E1 has radius half its length, so map-rad(X, E1) ≤ diam(X)/2. On the
other hand, for x, y ∈ X, the function d(x,−) : X → E1 is nonexpansive, and the images of x and y under
this map are d(x, y) apart, whence the radius of f(X) is at least half this value. Taking the supremum
over all x and y, we get map-rad(X, E1) ≥ diam(X)/2.
The next four steps, inequalities among mapping radii, are instances of Lemma 7(iii). In the equality
following these, the direction “≤” simply says that nonexpansive maps are radius-nonincreasing, while “≥”
holds because one of the maps in the supremum defining map-rad(X, Metr) is the identity map of X. The
final inequality is immediate.
We note in passing some cases where these mapping radii are easy to evaluate.
Corollary 10. If a metric space X satisfies radX(X) = diam(X)/2, then all terms of (15) through
radX(X) are equal (and hence also equal to all terms of (14)).
In particular, this is true whenever (i) X is a finite tree, with edges of arbitrary positive lengths, under
the arc-length metric, or (ii) X has an isometry ρ with a fixed point 0 such that for every x ∈ X,
d(x, ρ(x)) = 2 d(x, 0).
Proof. The first sentence is clear from (15). To get the two classes of examples, it suffices to show in each
case that radX(X) ≤ diam(X)/2, since (15) gives the reverse inequality.
In case (i), X is compact, so we may choose x, y ∈ X with d(x, y) = diam(X). The unique non-self-
intersecting path between x and y is isometric to a closed interval, and so has a midpoint p, satisfying
d(x, p) = d(y, p) = diam(X)/2; it now suffices to show that d(z, p) ≤ diam(X)/2 for all z ∈ X. Consider
the unique non-self-intersecting path from p to z. Because X is a tree, that path out of p cannot have
nontrivial intersection with both the path from p to x and the path from p to y; assume it meets the
latter only in p. Then the unique non-self-intersecting path from z to y is the union of the path from z to
p and the path from p to y, and we know that it has length ≤ diam(X), so subtracting off diam(X)/2,
the length of the path from p to y, we conclude that the length of the path from z to p is ≤ diam(X)/2,
as required.
In case (ii), we have radX(X) ≤ supx∈X d(x, 0) = supx∈X d(x, ρ(x))/2 ≤ diam(X)/2.
Examples falling under case (ii) above include all centrally symmetric subsets of normed vector spaces
containing 0, under the induced metric, and a hemisphere under the geodesic metric.
A less trivial result, now. Recall that in proving the upper bounds on the mapping radii of Lemmas 2,
3 and 4, we in effect chose formal weighted combinations of points of X, and used these to specify convex
linear combinations of points of f(X) ⊆ V. We abstract this technique below. In the statement of the
theorem, as a convenient way to express formal weighted combinations of points of X, we use probability
measures on X with finite support. (Recall that a probability measure on X is a nonnegative-valued
measure µ such that µ(X) = 1, and that µ is said to have support in a set X0 if it is zero on every subset
of X −X0. Apologies for the double use of “d” below, for the distance function of the metric space and the
“d” of integration.)
Theorem 11. Let X be a nonempty metric space. Then
(16) map-rad(X,Conv) = infµ supx∈X
d(x, z) dµ(z),
where the infimum is over all probability measures µ on X with finite support.
Proof. We first prove “≤”, imitating the argument of Lemmas 2, 3 and 4. We must show, for any nonexpan-
sive map f : X → C, where C is a convex subset of a normed vector-space V, and any probability measure
µ on X with finite support, that
(17) radC(f(X)) ≤ supx∈X
d(x, z) dµ(z).
For any point x of X, let µx denote the probability measure on X with singleton support {x}.
Since the µ of (17) is a probability measure with finite support, it has the form c1µx1 + · · · + cnµxn ,
where x1, . . . , xn are points of X, and c1, . . . , cn are nonnegative real numbers summing to 1. The point
ci f(xi) lies in C, so by definition of the radius, the left-hand side of (17) is ≤ supx∈X d(y, f(x)) =
supx∈X d(
ci f(xi), f(x)), which by (5) is ≤ supx∈X
ci d(f(xi), f(x)), which, because f is nonex-
pansive, is ≤ supx∈X
ci d(x, xi). The sum in this expression is the integral in (17), giving the desired
inequality.
In proving the direction “≥” in (16), we may assume the metric space X is bounded, since otherwise
it has infinite diameter, in which case (15) tells us that the left hand side of (16) is infinite. Assuming
boundedness, we shall display a particular embedding e of X in a convex subset C of a normed vector
space U, such that radC(e(X)) is greater than or equal to the right-hand side of (16).
Let U be the space of all continuous bounded real-valued functions on X, under the sup norm, let
e : X → U take each x ∈ X to the function d(x,−) (this e is easily seen to be nonexpansive) and let C
be the convex hull of e(X). Now for x ∈ X, its image e(x) = d(x,−) can be written y 7→
d(y, z) dµx(z).
Hence an arbitrary u ∈ C, i.e., a convex linear combination of these functions, will have the same form, but
with µx replaced by a convex linear combination µ of the measures µx, i.e., a general probability measure
µ on X with finite support. For such a function u, and any x ∈ X, the distance d(e(x), u) in C is the
sup norm of u− e(x), which is at least the value of u− e(x) at x ∈ X, which is u(x)− 0 =
d(x, z) dµ(z).
The radius of e(X) in C is thus at least the infimum over all µ of the supremum over all x of this integral,
which is the right-hand side of (16).
Recall that when we obtained our bound (7) on the mapping radius of the 1-skeleton of an octahedron,
analogy and good luck led us to the formal linear combination of points of X used in (6) (in effect, a
probability measure µ), which turned out to give the optimal bound. In general we ask
Question 12. Let X be a finite graph with edges of possibly unequal lengths, under the arc-length metric.
Must there be a probability measure µ on X with finite support that realizes the infimum of (16)?
Is there an algorithm for finding such a µ if it exists, or if not, for evaluating (16)?
We cannot expect in general that a measure of the desired sort will have support in the set of vertices of
the graph X, as happened in Lemma 4. E.g., if X is isometric to a circle with arc-length metric, one can
show that a measure µ realizes the infimum of (16) if and only if it gives equal weight to p and q whenever
p and q are antipodal points; so if X is, say, an equilateral polygon with an odd number of vertices, µ
cannot be concentrated in the vertices.
A class of examples generalizing our octahedral skeleton, which it would be of interest to examine, are
the 1-skeleta of cross polytopes [7].
A situation simpler than that of Question 12 is that of a finite metric space X. Here the determination
of the right-hand side of (16) is a problem in linear programming; whether it has an elegant solution I don’t
know. The determination of map-rad(X,En) for such a space X is, similarly, in principle, a problem in
calculus.
In Corollary 10, we saw that the mapping radius is easy to compute for a space that has “a robust
center”. Using the preceding theorem, let us show the same for a space with a pair of “robust antipodes”.
Corollary 13. Suppose the metric space X has a pair of points p and q such that
(18) (∀ r ∈ X) d(p, r) + d(r, q) = d(p, q).
Then letting D = d(p, q), we have diam(X) = D, and map-rad(X,Conv) = D/2. Thus, the terms of (15)
through map-rad(X,Conv) are all equal to D/2.
In particular, this is true if X is the 1-skeleton of a regular tetrahedron or of a parallelopiped (in
particular, of a cube), with the arc-length metric, or is the 0-skeleton of any of the regular polyhedra other
than the tetrahedron, with metric induced by the arc-length metric on the 1-skeleton of that polyhedron.
The property (18) is, of course, inherited by any subspace of X containing p and q.
Proof. For any two points r, r′ ∈ X, we have
2 d(r, r′) ≤ (d(r, p)+d(p, r′))+(d(r, q)+d(q, r′)) = (d(p, r)+d(r, q))+(d(p, r′)+d(r′, q)) = 2D,
so d(r, r′) ≤ D, whence diam(X) = D. Now let µ be the probability measure giving weight 1/2 to each
of p and q. For this µ, the integral on the right-hand side of (16) has value D/2 for all x, hence the
supremum of that integral over x is D/2, hence (16) shows that map-rad(X,Conv) ≤ D/2. Comparing
with the first term of (15), we see that all the the terms of (15) through map-rad(X,Conv) (though not,
as before, through map-rad(X,Metr)) are equal.
For X the 1-skeleton of a regular tetrahedron, we get (18) on taking for p and q the midpoints of
two opposite edges. For X the 1-skeleton of a parallelopiped, we can use any two antipodal points (not
necessarily vertices. In picturing this case, it may help to note that X is isometric to the 1-skeleton of a
rectangular parallelopiped.) In the 0-skeleton cases, we use any pair of opposite vertices. In each case, the
verification of (18) is not hard.
The final sentence is clear.
So, for instance, for the 1-skeleta of the tetrahedron and cube of edge 1, the 6-tuples of terms of (15) (not
distinguishing terms shown connected by equals-signs) are (1, 1, 1, 1, 3/2, 2) and (3/2, 3/2, 3/2, 3/2, 3, 3)
respectively. (The reason the last two numbers are equal for the cube, but distinct for the tetrahedron, is
that for the cube, the function x 7→ supy d(x, y) is 3 for all x, while for the tetrahedron, it ranges from
a maximum value 2 at the midpoints of the edges to a minimum value 3/2 at the vertices. In neither of
these cases is the maximum twice the minimum, so neither of them falls under Corollary 10.)
Let us note a curious feature of the construction used in Theorem 11: it has what at first looks like a
universal property (part (i) of the next result) but turns out not to be (part (ii)).
Corollary 14 (to proof of Theorem 11). Let X be a bounded metric space, let U be the space of continuous
bounded real-valued functions on X under the sup norm (cf. second half of the proof of Theorem 11), and
let e : X → U be the map taking each x ∈ X to the function d(x,−).
Now let f : X → V be any map (in Metr) from X into a normed vector space V. Then
(i) For every family of points x1, . . . , xn ∈ X, every family c1, . . . , cn of nonnegative real numbers summing
to 1, and every x ∈ X, one has
(19) d(f(x),
ci f(xi)) ≤ d(e(x),
ci e(xi)).
However,
(ii) Given points x1, . . . , xn ∈ X, and two families of nonnegative real numbers b1, . . . , bn and c1, . . . , cn,
each summing to 1, it is not necessarily true that
(20) d(
bi f(xi),
ci f(xi)) ≤ d(
bi e(xi),
ci e(xi)).
Thus, the convex hull of e(X) need not admit a map (in Metr) to the convex hull of f(X) making a
commuting triangle with e and f.
Proof. (i) may be seen by combining the calculations of the last sentence of the proof of the “≤” direction
of Theorem 11, which shows that d(f(x),
ci f(xi)) ≤
ci d(x, xi), and the end of the proof of the “≥”
direction, which, by evaluating e(xi) and e(x) as elements of the function-space U at the point x, shows
that d(e(x),
ci e(xi)) ≥
ci d(x, xi).
To get (ii), let X again be a circle of circumference 4 with arc-length metric, and let x0, x1, x2, x3 ∈ X
be four points equally spaced around it. Note that for any u ∈ X, we have d(x0, u) + d(x2, u) = 2 =
d(x1, u)+d(x3, u). Hence if we choose the bi and ci so that the right-hand-side of (20) is d((e(x0)+e(x2))/2,
(e(x1)+e(x3))/2), we see that this value is 0. On the other hand, if we map X into E
1 by f(x) =
1 − max(d(x0, x), 1), then of the f(xi), only f(x0) is nonzero, so the left-hand side is not 0, so (20)
fails.
There are, in fact, a different normed vector space U and mapping e : X → U for which the universal
property of (20) does hold [23, Theorem 2.2.4]; we examine this construction in an appendix, §6.
3 Some explicit mapping radii.
A classical result of H. E.W. Jung is, in effect, an evaluation of the mapping radius in En of a very simple
metric space.
Theorem 15 (after Jung [17]). Let D∞ denote an infinite metric space in which the distances between
distinct points are all 1. (The cardinality does not matter as long as it is infinite.) Then the values of
map-rad(D∞, E
n) for n = 0, 1, 2, . . . are, respectively,
(21) 0 < 1/2 < 1/
3/8 < . . . <
n/(2(n+ 1)) < . . . .
Hence, map-rad(D∞, Euc) = 1/
Likewise, for any positive integer m, if we let Dm be an m-element metric space with all pairwise
distances 1, then for every n ≥ 0,
(22) map-rad(Dm, E
r/(2(r + 1)) , where r = min(m−1, n).
Hence, map-rad(Dm, Euc) =
(m− 1)/(2m) .
Summary of proof. The main result of [17] is that every subset of En of diameter ≤ 1 has radius ≤
n/(2(n+ 1)) . This gives map-rad(D∞, E
n/(2(n+ 1)) . On the other hand, the n+ 1 vertices of
the n-simplex of edge 1 in En form a subset of radius exactly
n/(2(n+ 1)) , and clearly D∞ can be
mapped onto that set, establishing equality. Taking the limit of this increasing sequence as n → ∞, one
gets map-rad(D∞, Euc) = 1/
Clearly, the hypothesis m > n works as well as m =∞ in concluding as above that map-rad(Dm, En) =
n/(2(n+ 1)) . For m ≤ n, on the other hand, any image of Dm in En lies in an affine subspace that can be
identified with Em−1, so in that case we get map-rad(Dm, E
n) = map-rad(Dm, E
m−1) =
(m− 1)/(2m) .
Combining these results, we get (22) and the final conclusion.
The inequalities (21) show that each step of (14) can be strict. What about the steps of (15)? If we
identify terms connected by equal-signs, then (15) lists six possibly distinct values, connected by five ≤-signs.
Three of these ≤-signs are shown strict by the 3-point metric space D3 of the above theorem, for which,
I claim, the 6-tuple of values is (1/2, 1/
3 , 2/3, 2/3, 1, 1). The first of these values, and the last two,
are clear, and the second comes from the above theorem (line after (22)). To evaluate the remaining two
values, map-rad(D3, NmV) and map-rad(D3,Conv), consider the embedding e : D3 → U as in the last
paragraph of the proof of Theorem 11. The space U used there can in this case be described as R3 under
the sup norm; let C be the convex hull in U of
(23) e(D3) = {(0, 1, 1), (1, 0, 1), (1, 1, 0)}.
Then Lemma 7(ii) (in particular, the final sentence) tells us that radC(e(D3)) is the common distance of
the three points of e(D3) from the unique point of C invariant under cyclic permutation of the coordinates,
namely (2/3, 2/3, 2/3). This common distance is 2/3 (since each member of e(D3) has a zero coordinate),
so radC(e(D3)) = 2/3, and by Theorem 11, this is map-rad(D3,Conv). Since map-rad(D3, NmV) ≤
map-rad(D3, Conv), to show that map-rad(D3, NmV) is also 2/3 it will suffice to obtain a nonexpansive
map f of D3 into a vector space V such that radV (f(D3)) = 2/3. This may be done by using the same
mapping as above, but translated by (−2/3,−2/3,−2/3), so that the affine span of its image becomes a
vector subspace of R3, which, with its induced norm, we take as our V. The preceding argument now gives
radV (f(D3)) = 2/3.
For a space showing strict inequality at the final step of (15), radX(X) ≤ diam(X), one can use any
nontrivial instance of Corollary 10; for instance, the unit interval [0, 1], for which that corollary shows that
the 6-tuple in question is (1/2, 1/2, 1/2, 1/2, 1/2, 1).
This leaves the step
(24) map-rad(X,NmV) ≤ map-rad(X,Conv).
I thought at first that equality had to hold here: that for a C a convex subset of a normed vector space V
and any A ⊆ C (in particular, the image of any map of a metric space into C), one had radV (A) = radC(A).
However, this is not so: consider the untranslated case (23) of the above D3 example, and note that the point
(1/2, 1/2, 1/2) ∈ U has distance 1/2 from each point of (23); so radU (e(D3)) ≤ 1/2 < 2/3 = radC(e(D3)).
Nonetheless we have seen that for X = D3, equality holds in (24). Here, however, is an example (which
it took attempts spread over many months to find) for which that inequality is strict.
Consider the graph with 7 vertices, x, y0, y1, y2, z0, z1, z2, and 9 edges: a length-1 edge from x to each
of the yi, and a length-2 edge from yi to zj whenever i 6= j; and let X be the vertex-set of this graph,
with arc-length metric. Thus, for all i 6= j we have
(25) d(x, yi) = 1, d(x, zi) = 3, d(yi, yj) = 2,
d(yi, zj) = 2, d(yi, zi) = 4, d(zi, zj) = 4.
Let us first find map-rad(X,Conv), using Theorem 11. We must maximize the infimum (16) over the
convex linear combinations of µx, . . . , µz2 . By Lemma 5, it suffices to maximize that expression over points
invariant under permutations of the subscripts; i.e., over convex linear combinations of
(26) µx, µy = (µy1 + µy2 + µy3)/3, µz = (µz1 + µz2 + µz3)/3.
We find that
(27) µx(x) = 0, µx(yi) = 1, µx(zi) = 3,
µy(x) = 1, µy(yi) = 4/3, µy(zi) = 8/3,
µz(x) = 3, µz(yi) = 8/3, µz(zi) = 8/3.
Any convex linear combination of these three functions has value ≥ 8/3 at each zi; so every value of the
supremum in (16) is at least 8/3. Moreover, taking µ = µy (or more generally, µ = (1− t)µy + tµz for any
t ∈ [0, 5/6]), we see that this value 8/3 is attained; so
(28) map-rad(X,Conv) = 8/3.
The idea of our verification that map-rad(X,NmV) is strictly smaller than (28) will be to use the non-
convex affine combination (3µy − µx)/2 of the functions (27), so as to reduce somewhat the highest values
of µy, those at the zi, without bringing the values at other points up by too much. But since we don’t have
the analog of Theorem 11 for non-convex combinations (and indeed, that analog is not true in general – if it
were, then 2µy − µx would lead to a still better result, but it does not), we must calculate by hand rather
than calling on such a theorem. So suppose f is a nonexpansive map of X into a normed vector space V,
and let
(29) p = (f(y0) + f(y1) + f(y2)− f(x))/2.
We need to bound the distances between p and the points of f(X). In view of the symmetry of (29), it will
suffice to bound the distances to f(x), f(y0) and f(z0). We calculate
(30) d(p, f(x)) = || (f(y0) + f(y1) + f(y2)− f(x)− 2f(x))/2 ||
≤ (||f(y0)− f(x)||+ ||f(y1)− f(x)|| + ||f(y2)− f(x)||)/2 ≤ (1 + 1 + 1)/2 = 3/2.
d(p, f(y0)) = || (f(y0) + f(y1) + f(y2)− f(x)− 2f(y0))/2 ||
≤ (||f(y1)− f(y0)||+ ||f(y2)− f(x)||)/2 ≤ (2 + 1)/2 = 3/2.
d(p, f(z0)) = || (f(y0) + f(y1) + f(y2)− f(x)− 2f(z0))/2 ||
≤ (||f(y0)− f(x)||+ ||f(y1)− f(z0)||+ ||f(y2)− f(z0)||)/2 ≤ (1 + 2 + 2)/2 = 5/2.
Taking the maximum of these values, we get
(31) map-rad(X,NmV) ≤ 5/2 < 8/3 = map-rad(X,Conv),
a strict inequality, as claimed.
The above observations suggest the question: Which normed vector spaces V have the property that
the radius of every subset X of V is the same whether evaluated in V, or in an arbitrary convex subset of
V containing X ? This is examined in an appendix, §7.
The example of (23) showed that the radius of a subset of a normed vector space could change when one
passed to a larger normed vector space. Let us note a curious consequence.
Lemma 16. Let U be R3 under the sup norm, and U0 ⊆ U be {(a, b, c) ∈ U | a + b + c = 0}. Then
there is no isometric reflection U → U having U0 as its fixed subspace. In fact, no finite group of affine
isometries of any normed vector space W containing U has U0 as its fixed subspace.
Proof. Let W be any normed vector space containing U, and let f : D3 → U0 be given by f(x) =
e(x) − (2/3, 2/3, 2/3), for e as in the paragraph containing (23). The first sentence of Lemma 7(ii) gives
radW (f(D3)) ≤ radU (f(D3)), which we saw is < radU0(f(D3)). On the other hand, if W had a finite group
G of affine isometries with fixed subspace U0, then Lemma 5 would give radW (f(D3)) = radU0(f(D3)).
Returning to (14) and (15), let us for simplicity reduce the number of independent values by “normalizing”
to the case diam(X) = 2, and ask for more detailed information than those inequalities.
Question 17. Let X run over all metric spaces of diameter 2. What can one say about the geometry of
the resulting sets of sequences
(32) {(map-rad(X,E1), map-rad(X,E2), . . . , map-rad(X,En), . . . )} ⊆ RN,
(33) {(map-rad(X, Euc), map-rad(X, NmV), map-rad(X, Conv), map-rad(X, Metr))} ⊆ R4 ?
Can one describe them exactly? Are they convex; or do they become convex on replacing the entries by
their logarithms, or under some other natural change of coordinates?
If two successive terms of a member of (32) are equal, is the sequence constant from that point on?
Another family of questions, suggested by Theorem 15, is
Question 18. For n ≥ 2, what can one say about the set of nonnegative real numbers that can be written
map-rad(X,En) for finite metric spaces X in which all distances are integers?
Are all such real numbers “constructible”, i.e., obtainable from rational numbers by a finite sequence of
square roots and ring operations?
Is this set well-ordered for each n ? (It has a smallest element 0, and a next-to-smallest element 1/2.)
Does this set change if “finite metric spaces X . . . ” is weakened to “bounded metric spaces X . . . ”?
For m < n, can one assert any inclusion between the sets of mapping radii into Em, and into En ? Are
there values that occur as map-rad(X,Euc) for some X, but not as map-rad(X ′,En) for any X ′ and n ?
(E.g., can 1/
2 be written in the latter form?)
We end this section with an observation made in [12] for the spaces En, which in fact holds for closed
convex subsets of arbitrary finite-dimensional normed spaces.
Lemma 19 (cf. [12, Proposition 29, p.14, and second paragraph of p.46]). If C is a closed convex subset of
a normed vector space V of finite dimension n, and A a subset of C with > n elements, then radC(A) =
supA0 radC(A0), where A0 runs over the n+1-element subsets of A.
Proof. “≥” is clear; so it suffices to show that if for some real number r, each A0 is contained in a closed
ball of radius r centered at a point of C, then so is A. Now for each a ∈ A, the set of v ∈ C such that
a lies in the closed ball in C of radius r about v is the closed ball in C of radius r about a, hence a
compact convex subset of V. To say that a set A0 is contained in some closed ball of radius r centered
at a point of C is to say that the intersection of these sets, as a runs over A0, is nonempty. By Helly’s
Theorem ([14], [8]), if a family of compact convex subsets of Rn has the property that every system of n+1
members of this family has nonempty intersection, then so does the whole family; which in this case means
that all of A is contained in a ball of the indicated sort.
The above lemma does not imply the corresponding statement for mapping radii. For example, let
X = {x, y0, y1, y2}, where x has distance 1/2 from each of the yi, and these have distance 1 from each
other. The maximum of the mapping radii in E2 of 3-element subsets of X is map-rad({y0, y1, y2},E2) =
map-rad(D3,E
2) = 1/
3. But map-rad(X,E2) ≤ radX(X) = 1/2.
On the other hand, for this example, map-rad(X,E2) can be described as the infimum over p ∈ X of
the supremum of map-rad(X0,E
2) over all 3-element subsets X0 of X containing p. So we ask
Question 20. Does there exist, for every positive integer n, a positive integer N and a formula which
for every metric space X of ≥ N elements, and every normed vector space V of dimension n, expresses
map-rad(X,V ), using the operations of suprema and infima, in terms of the numbers map-rad(X0, V ), for
N -element subsets X0 ⊆ X ?
4 Realizability of mapping radii.
For a subset A of a metric space Y, let us say that radY (A) is realized if the infimum in the definition (2)
of that expression is attained, that is, if there exists y ∈ Y such that A is contained in the closed ball of
radius radY (A) about y.
Likewise, for metric spaces X and Y, let us say that map-rad(X,Y ) is realized if the supremum in the
definition of that expression is attained; that is, if there exists an f : X → Y such that radY (f(X)) =
map-rad(X,Y ). (This does not presume that radY (f(X)) is realized.)
Lemma 21. Let X and Y be nonempty metric spaces.
(i) If Y is compact, then for any subset A ⊆ Y, radY (A) is realized.
(ii) If X and Y are both compact, then map-rad(X,Y ) is realized.
However
(iii) For X compact and Y bounded and complete, or for X bounded and complete and Y compact,
map-rad(X,Y ) may fail to be realized.
Proof. (i) follows from the fact that for bounded A, supa∈A d(a, y) is a continuous function of y, hence
assumes a minimum on Y.
To get (ii), we note that Metr(X,Y ) is a closed subset of the function space Y X , which is compact
because Y is, so Metr(X,Y ) is compact in the function topology. We would like to say that the real-valued
map on this space given by f 7→ radY (f(X)) is continuous, and hence assumes a maximum. For general
X, this continuity does not hold, as will follow from the second statement of (iii); but I claim that it holds if
X is compact. For given f ∈Metr(X,Y ) and ε > 0, compactness allows us to cover X by finitely many
open balls of radius ε/3, say centered at x1, . . . , xn. Consider the neighborhood of f in Metr(X,Y ) given
U = {g ∈Metr(X,Y ) | d(f(xi), g(xi)) < ε/3 (i = 1, . . . , n)}.
Taking any x ∈ X and y ∈ Y, note that there exists i such that d(xi, x) < ε/3; hence for g ∈ U,
|d(f(x), y) − d(g(x), y)| ≤ d(f(x), g(x)) ≤ d(f(x), f(xi)) + d(f(xi), g(xi)) + d(g(xi), g(x))
≤ ε/3 + ε/3 + ε/3 = ε.
Thus, the two functions associating to every y ∈ Y the numbers supx∈X d(f(x), y) and supx∈X d(g(x), y)
differ everywhere by ≤ ε, whence the infima of these functions, radY (f(X)) and radY (g(X)) differ by ≤ ε,
giving continuity of f 7→ radY (f(X)), which, as noted above, yields (ii).
(iii) For an example with X but not Y compact, let X = D2, i.e., a space consisting of two points
at distance 1 apart, and let Y = {y2, y3, . . . , yn, . . . } ∪ {z}, with d(ym, yn) = 1 (m 6= n) and d(yn, z) =
1 − 1/n. Note that the radius in Y of a point-pair {z, yn} or {ym, yn} with m < n is 1 − 1/n. Now
Metr(X,Y ) consists of all set-maps X → Y, and it follows from the above calculation that map-rad(X,Y ) =
supn(1− 1/n) = 1, but that this value is not achieved. (If we had not specified that Y should be complete,
we could have used the simpler example, X = D2, Y = [0, 1).)
For an example with Y but not X compact, let X = {x2, x3, . . . , xn, . . . }, with d(x2n, x2n+1) = 2−1/n,
and all other pairs of distinct points having distance 1; and let Y = [0, 2] ⊆ E1. Note that if a map X → Y
is to have radius > 1/2, it must send some pair of points to values differing by > 1, and by our metric
on X, these two points must have the forms x2n, x2n+1. Since all other points have distance 1 from these
two, the images of all other points must fall within the interval between their images. Hence the image of
our map falls within an interval of length ≤ 2− 1/n for some positive n, i.e., of length < 2, and hence of
radius < 1. But such images can have radii arbitrarily close to 1, again giving a mapping radius that is not
realized.
Corollary 22. Suppose Y is a metric space in which every closed bounded subset is compact. Then
(i) For every bounded nonempty subset A ⊆ Y, radY (A) is realized.
(ii) If the isometry group of Y is transitive, or more generally, if Y has a bounded subset which meets
every orbit of that group, then for every compact nonempty metric space X, map-rad(X,Y ) is realized.
Proof. (i) Let radY (A) = r, choose any a0 ∈ A, and let Y ′ be the closed ball of any radius r′ > diam(A) ≥
r about a0 in Y. By assumption Y
′ is compact. We see that A ⊆ Y ′, and that every point y ∈ Y with
supa∈A d(a, y) ≤ r′ lies in Y ′. Since infy∈Y supa∈A d(a, y) = r, the space Y contains points y for which
supa∈A d(a, y) comes arbitrarily close to r; hence it will contain points for which that value is arbitrarily
close to r and is ≤ r′. Points with this latter property lie in Y ′, whence radY ′(A) is also equal to r, and
applying part (i) of the preceding lemma with Y ′ for Y gives the desired conclusion.
(ii) Suppose every orbit of the isometry group of Y meets the closed ball of radius c about y0 ∈ Y, and
let x0 be any point of X. Then every f : X → Y may be adjusted by an isometry of Y (which will preserve
the radius of f(X)) so that we get d(f(x0), y0) ≤ c, and after this adjustment, f(X) will lie in the closed
ball of radius c + diam(X) about y0. Letting Y
′ denote the closed ball of any radius r′ > c + diam(X)
about y0, we see as in the proof of (i) that the radii of these image sets f(X) in Y
′ will equal their radii
within Y, and applying part (ii) of the preceding lemma with Y ′ for Y, we get the desired conclusion.
5 Related literature (and one more question).
Lemma 2 above, determining the mapping radius of a circle in a normed vector space V, occurs frequently
in the literature (with E3 or En for V ) as an offshoot of the proof of Fenchel’s Theorem, the statement
that the total curvature of a closed curve C in E3 is at least 2π, with equality only when C is planar
and convex [9, Satz I]. To prove that theorem, Fenchel noted that this total curvature is the length of the
curve in the unit sphere S2 traced by the unit tangent vector to C, and that that curve cannot lie wholly
in an open hemisphere of S2 (nor in a closed hemisphere unless C is planar). He completed the proof by
showing [9, Satz I′ ] that a closed curve of length < 2π (respectively, equal to 2π) in S2 must lie in an
open hemisphere (respectively, must either lie in an open hemisphere or be a union of two great semicircles).
In our language, this says that a circle of circumference < 2π, made a metric space using arc-length, has
mapping radius < π/2 in S2 and (along with some additional information) that the circle of arc-length
exactly 2π has mapping radius π/2.
Subsequent authors [5], [6, Lemma on p.30], [16], [19], [21], [22] gave simpler proofs of Fenchel’s Satz I′
(similar to our proof of Lemma 2), and/or generalized that result from S2 to Sn, and/or obtained the more
precise result that the mapping radius of a circle of length L ≤ 2π in Sn is L/4, and/or noted that the
same method also gives the analogous result with En, or indeed any of a large class of geometric structures,
in place of Sn.
The last-mentioned generalizations were based on the observation that the concept of the midpoint of a
pair of points can be defined, and behaves nicely, in many geometric contexts. I do not know whether more
general convex linear combinations, such as we used in (5) and in the proof of Theorem 11, can be defined
outside the context of vector spaces so as to behave nicely; hence the emphasis in this note on vector spaces
and their convex subsets. A.Weinstein (personal communication) suggests that an approach to “averaging”
of points introduced by Cartan and developed further by Weinstein in [24] might serve this function. J.Lott
(personal communication) points similarly to the concepts of Hadamard space [2] and Busemann convex
space [4].
The results on closed curves of length L in the unit sphere cited above all take L ≤ 2π. If we write S1L
for a circle of circumference L with arc-length metric, and Sn for the unit n-sphere (of circumference 2π)
with geodesic distance as metric, it is clear that the result map-rad(S1L, S
n) = L/4 cannot be expected to
hold when L > 2π; but it would be interesting to investigate how that mapping radius does behave as a
function of L. For all L, map-rad(S1L, S
n) < π, since a curve of fixed length cannot come arbitrarily close
to every point of Sn, and if it misses the open disk of geodesic radius r about a point p, then it is contained
in the closed disc of geodesic radius π − r about the antipodal point.
Many of the papers referred to above consider arcs as well as closed curves; i.e., also study map-rad([0, L],
Sn), and prove that for L ≤ π, this equals L/2. Again, the case of larger L would be of interest. So we
Question 23. For fixed n > 1, how does map-rad(S1L, S
n) behave for L > 2π, and how does map-rad([0, L],
Sn) behave for L > π, as functions of L ?
For instance, are these two functions piecewise analytic?
It seems likely that there will be ranges of values of L in which different configurations of a closed curve or
arc give maximum radius, and that the value of this radius will be an analytic function of L within each such
range. (I conjecture that for all L between 2π and a value somewhat greater than 3π, map-rad(S1L, S
will be realized by a “3-peaked crown”, consisting of 6 arcs of great circles, with midpoints equally spaced
along a common equator. For map-rad(S1L, S
n) with n > 2, I have no guesses.)
Many papers in this area also consider the smallest “box” – in various senses – into which one can fit
all curves, or closed curves, of unit length [5] [13] [20], or all point-sets of unit diameter [10]. These do not
translate into statements about our concept of mapping radius for two reasons. First, they deal with arc
length in the Euclidean metric, but with “boxes” which, though they could in many cases be considered
closed balls in another metric, are not balls in the Euclidean metric; and our formalism of mapping radius
does not look at more than one metric on Y at a time. Second, they generally allow rotations as well as
translations in fitting the box around the curve, while in looking at radii we only have one closed ball of
each radius centered at a given point.
The intuitive interest of Question 23 above arises in part from a special property of the sphere: that a
large open or closed ball, i.e., one that falls just short of covering Sn, has for complement a small closed or
open ball. For spaces Y not having this property, the most natural analogs of those questions might be the
corresponding questions about “mapping co-radii”, given by the definitions
(34) coradY (A) = supy∈Y infa∈A d(a, y) (A ⊆ Y ),
(35) map-corad(X,Y ) = inff∈Metr(X,Y ) coradY (f(X))
= inff∈Metr(X,Y ) supy∈Y infx∈X d(f(x), y)
(cf. (2) and (3)). So, for instance, one might ask about the values of map-corad([0, L], B2) for B2 the closed
unit disc in R2, as a function of L.
(I’m not sure that “co-radius” is a good choice of term: one could argue that that term would more ap-
propriately apply either to radY (Y−A), or to what in the notation of (34) would be written coradY (Y −A).
So the above names are just suggestions, which others may choose to revise.)
6 Appendix: The Arens-Eells space of X.
At the end of §2, I mentioned that every metric space X admits an embedding in a normed vector
space U having the universal property that Corollary 14(ii) showed that the embedding we were considering
there did not have. The construction in question was introduced by Arens and Eells [1], and its universal
property noted by Weaver [23, Theorem 2.2.4], who calls it the Arens-Eells space of X. Weaver is there
most interested in this space as a pre-dual to the Banach space of Lipschitz functions on X. I will sketch
below a motivation for the same object in terms of the universal property. My description will also make a
couple of technical choices different from those of [1] and [23].
Essentially the same construction arises in mathematical economics, in the study of the “transportation
problem” [11], cf. [23, §2.3]. What to us will be the norm of an element of the Arens-Eells space appears
there as the minimum cost of transporting goods from a given set of sources to a given set of markets.
To lead up to the construction, let a metric space X be given, consider any map (as always, nonexpansive)
f of X into a normed vector space V, and let us ask, as a sample question: If we know the distances among
four points x1, x2, x3, x4 ∈ X, what can we say about ||f(x1) + f(x2)− f(x3)− f(x4)|| ?
Clearly, this will be bounded above by ||f(x1)−f(x3)|| + ||f(x2)−f(x4)|| ≤ d(x1, x3) + d(x2, x4). The
other way of pairing terms of opposite sign similarly gives the bound d(x1, x4) + d(x2, x3). Hence
(36) ||f(x1) + f(x2)− f(x3)− f(x4)|| ≤ min(d(x1, x3) + d(x2, x4), d(x1, x4) + d(x2, x3)).
For a similar, but slightly less straightforward case, suppose we want to bound ||3f(x1) + f(x2) −
2f(x3) − 2f(x4)||. We cannot, as before, pair off terms whose coefficients in this expression happen to be
the same except for sign. There are, however, ways of breaking up that expression as a linear combination
of differences; and a little experimentation shows that all ways of doing so are convex combinations of two
extreme decompositions. These two cases lead to the bound
(37) ||3f(x1) + f(x2)− 2f(x3)− 2f(x4)|| ≤
min(2d(x1, x3)+d(x1, x4)+d(x2, x4), 2d(x1, x4)+d(x1, x3)+d(x2, x3)).
We will not stop here to prove that (36) and (37) are best bounds. Let us simply observe that these
considerations suggest that the norm of such a linear combination of images of points of X under a universal
map e : X → U should be given by an infimum of linear combinations of the numbers d(x, y) (x, y ∈ X)
with nonnegative real coefficients, the infimum being taken over all such linear expressions which, when each
d(x, y) is replaced by e(x)− e(y), give the required element.
An obvious problem is that the only elements we get in this way are those in which the sum of the
coefficients of the members of e(X) is 0. This difficulty is intrinsic in the situation: There will not in fact
exist a nonexpansive map of X into a normed vector space having the standard sort of universal mapping
property with respect to such maps, because, though the condition of nonexpansivity bounds the distances
among images of points of X, it does not bound the distances between such images and 0; so universality
would force the images of points of X to have infinite norm.
What we can get, rather, is a set-map e of X into a vector space U, and a norm on the subspace U0
of linear combinations of images of points of X with coefficients summing to 0, such that for all x, y ∈ X,
||e(x) − e(y)|| ≤ d(x, y), and which has the universal property that given any nonexpansive map f of X
into a normed vector space V, there exists a unique vector-space homomorphism g : U → V which satisfies
f = ge, and is nonexpansive on U0. Observe that the norm on U0 induces a metric on each coset of that
subspace; in particular, on the coset U1 of elements in which the sum of all coefficients is 1, which is the
affine span of the image of X. The map of X into that coset is nonexpansive, and the asserted universal
property of e is easily seen to yield (20), the property that the construction of §2 failed to have.
Weaver’s answer to the same distance-to-0 problem is to use metric spaces with basepoint, and basepoint-
respecting maps, the basepoint of a vector space being 0. This has the advantage of giving a universal
property in the conventional sense, with both U and V in the category of normed vector spaces. However,
it requires one to make a possibly unnatural choice of basepoint in X ; changes in that choice induce isometries
on the universal space, which, though affine, are not linear. The approach I actually find most natural is
to regard what I have called U1 as a “normed affine space”, that is, a set with a simply transitive group
of “translation” maps by elements of a normed vector space, and to note that U1 has a genuine universal
property in the category of normed affine spaces. However, the development of that concept would be an
excessive excursion for this appendix. Still another approach would be to work with “normed” vector spaces
where the norm is allowed to take on the value +∞. In any case, it is straightforward to verify that the
Arens-Eells space of X as described in [23] and my U0 are isometrically isomorphic, so below I will quote
results of Weaver’s, tacitly restated for my version of the construction.
The details, now: let U be the vector space of all real-valued (i.e., not necessarily nonnegative) measures
µ on X with finite support, and, as before, for each x ∈ X let µx be the probability measure with support
{x}. Thus, {µx | x ∈ X} is a basis of U. Let U0 ⊆ U denote the subspace of measures µ satisfying
µ(X) = 0. Let W similarly denote the space of all real-valued measures on X × X with finite support;
for each (x, y) ∈ X ×X, let νx,y be the probability measure with support {(x, y)}, and let W+ ⊆ W be
the cone of nonnegative linear combinations of the νx,y, i.e., the nonnegative-valued measures on X ×X.
Finally, let D : W → U be the linear map defined by the condition
(38) D(νx,y) = µx − µy for x, y ∈ X,
which clearly has image U0. We now define the norm of any µ ∈ U0 by
(39) ||µ|| = inf
ν∈W+, D(ν)=µ
(x,y)∈X×X
d(x, y) dν .
It is easy to verify that this indeed gives a norm with the desired universal property. The one verification that
is not immediately obvious is that it is a norm rather than a pseudonorm; i.e., that it is nonzero for nonzero
µ ∈ U0. To get this, one first proves the desired universal property in the wider context of pseudonormed
vector spaces, then notes that given any nonzero µ =
ai µxi ∈ U0 (I finite, all ai nonzero), one can
find a nonexpansive map f : X → R which is zero at all but one of the xi, say xi0 , from which it follows
by the universal property that ||µ|| ≥ |
ai f(xi)| = |ai0 f(xi0)| > 0.
Weaver [23, Theorem 2.3.7(b)] shows that the infimum in (39) is always attained, and in fact, by a ν
whose “support” in X (the set of points which appear as x or y in terms νx,y having nonzero coefficient
in the expression for ν) coincides with the support of µ (the set of x such that µx appears with nonzero
coefficient in the expression for µ). Our next proposition strengthens this result a bit. For brevity, we will
call on Weaver’s result in the proof, but I will sketch afterward how the argument can be made self-contained.
We will use the following notation and terminology. Given ν =
bj νxj ,yj ∈ W (where J is a finite
set, the pairs (xj , yj) for j ∈ J are distinct, and all bj 6= 0), let Γ(ν) be the directed graph having for
vertices all points of X, and for directed edges the finitely many pairs (xj , yj) (j ∈ J). Let us define the
positive support of a directed graph Γ as the set of vertices which are initial points of its edges, and its negative
support as the set of vertices which are terminal points. For ν ∈ W, we will call the positive and negative
supports of Γ(ν) the positive and negative supports of ν. On the other hand, for µ =
ai µxi ∈ U0, let
us define its positive support to be {xi | ai > 0}, and its negative support to be {xi | ai < 0}. These are
clearly disjoint. Note that when ν ∈W+, the positive support of D(ν) is contained in the positive support
of ν, and contains all elements thereof that are not also in the negative support of ν, and that the negative
support of D(ν) has the dual properties.
When we speak of a cycle in a directed graph, we shall mean a cycle in the corresponding undirected
graph; we shall also understand that in a cycle no vertex is traversed more than once. Note that a cycle
of length 1 in Γ(ν) can only arise when a term νx,x has nonzero coefficient in ν, while a cycle of length
2, i.e., the presence of two edges between x and y, can only occur if νx,y and νy,x both have nonzero
coefficients. But a cycle of length n > 2 involving a given sequence of vertices may arise in any of 2n ways,
depending on the orientations of the edges.
We now prove
Proposition 24 (cf. [11, Theorem 3.3, p.84]). Let µ ∈ U0. Then the infimum of (39) is attained by an
element ν ∈W+ (not necessarily unique) whose positive and negative supports coincide respectively with the
positive and negative supports of µ, and whose graph Γ(ν) has no cycles.
Proof. As mentioned, Weaver proves the existence of a ν ∈ W+ with D(ν) = µ which achieves the infi-
mum (39) and has the same support as µ. Let ν be chosen, first, to have these properties; second, among
such elements, to minimize the total number of edges in Γ(ν), and finally, to minimize the sum of the
coefficients of all the νx,y in its expression. This last condition is achievable because the set of elements of
W+ which are linear combinations of a given finite family of the νx,y, and for which the coefficients of these
elements are all ≤ some constant, is compact; so after finding some ν with D(ν) = µ which achieves the
minimum (39), has the same support as µ, and minimizes the number of edges in Γ(ν), we may restrict our
search for elements also minimizing the coefficient-sum to the compact set of elements having these properties
and having every coefficient less than or equal to the coefficient-sum of the element we have found.
Suppose, now, that Γ(ν) has a cycle. Thus, we may choose distinct vertices p1, . . . , pk, and for each
j ∈ {1, . . . , k}, a term νpj ,pj+1 or νpj+1,pj occurring with positive coefficient in ν, where the subscripts j
are taken modulo k. (If k > 2 and both νpj ,pj+1 and νpj+1,pj occur in ν, we choose one of these arbitrarily.
If k = 2, we make sure that the terms we choose for j = 1, 2 are distinct, one being νp1,p2 and the other
νp2,p1 .) For each j ∈ {1, . . . , k} let us now define ν′pj ,pj+1 to be νpj ,pj+1 if that is the jth term in the
list we have chosen, or −νpj+1,pj if the jth term in that list is νpj+1,pj , and let ν′ =
ν′pj ,pj+1 ∈ W.
In general, ν′ /∈ W+, but for all λ ∈ R near enough to 0, we have ν + λν′ ∈ W+, since the relevant
coefficients in ν are strictly positive. Note that for each j, D(ν′pj ,pj+1) = µj − µj+1, hence D(ν
′) = 0,
hence D(ν + λν′) = D(ν) = µ.
Clearly,
(x,y)∈X×X
d(x, y) d(ν+λν′) is an affine function of λ. Hence it must be constant, otherwise,
using small λ of appropriate sign, we would get a contradiction to the assumption that ν achieves the
minimum of (39); so all the elements ν+λν′ achieve this same minimum. Now some choice of λ will cause
λν′ to exactly cancel the smallest among the coefficients of terms νpj ,pj+1 or νpj+1,pj in our cycle in Γ(ν).
Thus, ν + λν′ contradicts the minimality assumption on the number of edges in Γ(ν). This contradiction
shows that Γ(ν) has no cycles.
Next, let us compare the positive and negative supports of ν with those of µ. We have chosen ν so
that its support, namely the union of its positive and negative supports, coincides with the support of µ;
and since µ = D(ν), the positive and negative supports of ν will each contain the corresponding support
of µ. So if these inclusions are not both equalities, we must have a vertex p which is both in the positive
and the negative support of ν; i.e., such that there is an edge (q, p) of Γ(ν) leading into p, and an
edge (p, r) leading out of it. Let ν′ = νq,r − νq,p − νp,r. Like the element denoted by that symbol in the
preceding argument, this satisfies D(ν′) = 0. Let us again form ν + λ ν′, this time choosing the value
λ > 0 which leads to the cancellation of the smaller of the coefficients of νq,p and νp,r in ν, or of both
if these coefficients are equal. Since this does not reverse the sign of either of these coefficients, ν + λ ν′
still belongs to W+. Note that
(x,y)∈X×X
d(x, y) d(ν+λν′) ≤
(x,y)∈X×X
d(x, y) dν, since by the triangle
inequality, d(q, r) ≤ d(q, p) + d(p, r); so the property of minimizing the latter integral among elements of
W+ mapped to µ by D has not been lost. Also, Γ(ν + λν
′) has dropped at least one edge that belonged
to Γ(ν), since at least one coefficient was canceled, and has gained at most one edge, namely (q, r) (if that
was not previously present); so the total number of edges has not increased. Finally, when we look at the
sum of all the coefficients, we see that the coefficient of νq,r has increased by λ, while those of νq,p and
νp,r have both decreased by λ, so there has been a net change of −λ < 0. Thus, we have a contradiction to
our choice of ν as minimizing that sum. This completes the proof of the main assertion of the proposition.
Let us verify, finally, the parenthetical comment that the ν of the proposition may not be unique. Let
X be a 4-point space {x1, x2, x3, x4} where the distance between every pair of distinct points is 1, and let
µ = µx1 + µx2 − µx3 − µx4 . It is not hard to check that in this case, the only elements ν that can possibly
satisfy the conditions of the proposition are νx1,x3 + νx2,x4 and νx1,x4 + νx2,x3 . Since these give the same
value for the integral of (39), d(x1, x3) + d(x2, x4) = 2 = d(x1, x4) + d(x2, x3), each satisfies our conditions.
(Of course, for most choices of metric on this set X, one of these two values is smaller than the other,
and we then get a unique ν satisfying the conditions of the proposition.)
To get a self-contained version of the above proof which includes the existence result we cited from [23],
one may start by looking at any finite subset X0 of X containing the support of µ, verify by compactness
as above that the infimum of (39) over elements ν with support contained in X0 is achieved, then note
that any element in the support of ν but not in the support of µ must belong to both the positive and
negative supports of ν, a situation excluded by the proof. Letting X0 then run over all finite subsets of
X containing the support of µ, one sees that the infimum of (39) exists, and is simply the infimum with ν
restricted to have support in the support of µ.
We remark that the final condition of the above proposition, that Γ(ν) have no cycles, is not entailed
by the other conditions. E.g., returning to X = {x1, x2, x3, x4} with all distances 1, we see that every
convex linear combination ν of the two elements that we found, νx1,x3 + νx2,x4 and νx1,x4 + νx2,x3 , still
minimizes (39), and still has support X ; but if ν is a proper convex combination of those two elements,
then Γ(ν) = Γ(νx1,x3+ νx2,x4) ∪ Γ(νx1,x4+ νx2,x3), which contains (indeed, is) a cycle.
Let us now show, however, that when, as in the statement of the proposition, Γ(ν) is cycle-free, it
uniquely determines ν. Thus, the calculation of the norm (39) reduces in principle to checking finitely
many ν.
Lemma 25. Let µ ∈ U0, and let Γ be a directed graph with vertex-set X and without cycles. Then there
is at most one ν ∈W (and so, a fortiori, at most one ν ∈ W+) such that D(ν) = µ and Γ(ν) ⊆ Γ.
To characterize this element ν, consider any edge (x, y) in Γ. Let Γx (containing x) and Γy (containing
y) be the connected components into which the connected component of Γ containing (x, y) separates when
that edge is removed. Then the coefficient in ν of νx,y is the common value of
dµ and −
dµ, i.e.,
is both the sum of the coefficients of µz over z in Γx, and the negative of the corresponding sum over Γy.
Proof. We will prove the assertion of the second paragraph, from which that of the first clearly follows.
Writing µ = D(ν), the contributions to the expression
dµ from any term νp,q such that both p and
q lie in Γx clearly cancel, while terms such that neither p nor q lies in Γx contribute nothing. This leaves
the νx,y term, which contributes precisely its coefficient, leading to the first description of that coefficient.
Likewise, this term contributes the negative of its coefficient to
dµ, yielding the second description.
Corollary 26. Suppose µ ∈ U0 is integer-valued, and let ν be an element of W+ with the properties that
D(ν) = µ, that ν has the same positive and negative supports as µ, and that Γ(ν) has no cycles.
Then for every x ∈ X such that the coefficient of µx in µ is ±1, the vertex x is a leaf of Γ(ν).
Hence, if µ has the property that the coefficient of every µx is ±1, then ν is induced, in the obvious
way, by a bijection between the positive support of µ and the negative support of µ. Thus, in that case,
letting n be the common cardinality of these supports, there are exactly n! such ν ∈ W+.
Proof. Consider any x such that µx has coefficient +1 in µ. Then x is in the positive support of Γ(ν),
but not in the negative support. The latter condition says that ν involves no terms νy,x (y ∈ X), so +1
is the sum of the coefficients in ν of the terms νx,y (y ∈ X − {x}). Since ν ∈ W+, these coefficients are
nonnegative, and by Lemma 25 they are integers, so as they sum to 1, only one of them can be nonzero,
making x a leaf. The same argument, mutatis mutandis, gives the case where the coefficient of µx is −1.
The assertion of the final paragraph clearly follows, since a directed graph in which every vertex is a leaf
corresponds to a bijection between “source” and “sink” vertices.
As sample applications, recall the two computations at the beginning of this section, with which we
motivated the construction of our universal embedding e : X → U0. In our present notation, what we
were doing was evaluating the norms in U0 of elements of the two forms µx1 + µx2 − µx3 − µx4 and
3µx1 + µx2 − 2µx3 − 2µx4. In the first case, the last paragraph of the above corollary leads to just two
graphs, and hence two values of ν one of which must achieve the infimum (39), namely νx1,x3 + νx2,x4 and
νx1,x4 + νx2,x3 , showing that if f is our universal map e, equality holds in (36), and for general f, (36) is
the best bound. (This also establishes the example that we said was “not hard to check” in the next-to-last
paragraph of the proof of Proposition 24.)
In the case µ = 3µx1 + µx2 − 2µx3 − 2µx4, Corollary 26 says that x2 is a leaf of Γ(ν). As it lies in the
positive support of µ, the vertex it is attached to must lie in the negative support, i.e., must be either x3
or x4. In the former case, subtracting νx2,x3 from ν will give an element ν
′ ∈ W+ which is sent by D to
3µx1 − µx3 − 2µx4 . Since this ν′ has only one element, x1, in its positive support, its graph is uniquely
determined, giving ν′ = νx1,x3 +2νx1,x4 , hence ν = νx2,x3 + νx1,x3 +2νx1,x4 . The case where x2 is attached
to x4 similarly gives ν = νx2,x4 + 2νx1,x3 + νx1,x4 , and these together show that (37) is a best bound.
I referred earlier to the mathematical economist’s “transportation problem”. There, our d(x, y) corre-
sponds to the cost of transporting a unit quantity of goods from location x to location y; so our definition of
||µ|| describes the minimum cost of transporting goods produced and consumed at locations and in quantities
specified by µ.
Incidentally, the first assertion of Corollary 26 does not remain true if we weaken “the coefficient of µx
in µ is ±1” to “the coefficient of µx has least absolute value among the nonzero coefficients occurring in
µ.” For instance, suppose µ has the form 3µx1 − 4µx2 + 2µx3 − 4µx4 + 3µx5 . Then one of the elements of
W+ satisfying the conditions of Corollary 26 is ν = 3νx1,x2 + νx3,x2 + νx3,x4 + 3νx5,x4 . Here Γ(ν) has the
form x1 → x2 ← x3 → x4 ← x5, so x3, despite having smallest coefficient in µ, is not a leaf.
Proposition 24 sheds some light on our earlier “partial universality” result, Corollary 14(i). Given any
convex linear combination of points of our universal image of X,
aiµxi (ai > 0,
ai = 1), and any
point x ∈ X (which for simplicity we will assume is not one of the xi, though the argument can be adjusted
to the case where it is), the difference µx −
aiµxi is an element of U0 with positive support {x} and
negative support {xi | i ∈ I}. For this situation, the conditions of Proposition 24 clearly lead to a unique
Γ(ν), to wit, the tree whose edges are all pairs (x, xi) (i ∈ I), and hence to the unique choice ν =
ai νx,xi .
Thus, the right-hand side of (39) comes to
ai d(x, xi), which is equal to the right-hand side of (19). In
contrast, when one considers the difference between two general convex linear combinations of elements µx,
as in Corollary 14(ii), there may be many directed graphs satisfying the conditions of Proposition 24, so the
norm of that difference doesn’t have a simple expression.
The universality of the Arens-Eells space U yields a formula for map-rad(X,NmV) analogous to our
description (16) of map-rad(X,Conv); namely,
(40) map-rad(X,NmV) = infµ∈U1 supx∈X infν∈W+, D(ν)=µ−µx
y,z∈X
d(y, z) dν(y, z).
But this is cumbersome to use. E.g., the reader might try working through a verification, for the space
described by (25), of the statement that the µ implicit in (29), (µy0 + µy1 + µy2 − µx)/2, does indeed lead
to the infimum of (40), showing that map-rad(X,NmV) = 5/2, and not a smaller value.
Given an element µ ∈ U0, it would be interesting to look for bounds on the number of distinct graphs
Γ(ν) corresponding to elements ν ∈W+ as in the first sentence of Corollary 26. (This is simply a function
of the coefficients occurring in µ, as a family of positive real numbers with multiplicities.) To start with,
one might look for bounds in terms of the cardinalities of the positive and negative supports of µ.
Weaver [23] also gets a description of the universal nonexpanding map of X into a normed complex
vector space, paralleling the description for the real case, but he notes [23, p.43, next-to-last paragraph of
§2.2] that in the complex case it is no longer true that the infimum corresponding to (39) is always attained
by a ν having the same support as µ. (In the complex version of (39), by the way, one must replace dν by
|dν|, instead of restricting ν to a “positive cone” as above, since there is no natural analog of that cone.
Weaver takes this approach for both the real and complex cases; my use of W+ for the real case is one of
the different technical choices that I have made.) For instance, if X = {x, y0, y1, y2} with d(x, yi) = 1 and
d(yi, yj) = 2 (i 6= j), and if µ = µy0 + ωµy1 + ω2µy2 , where ω is a primitive cube root of unity, then the
minimizing ν is νy0,x + ωνy1,x + ω
2νy2,x, which makes that integral 3, while the best ν having support
in the support of µ, {y0, y1, y2}, is 13 (1− ω)νy0,y1 +
(ω − ω2)νy1,y2 + 13 (ω
2 − 1)νy2,y0 , of which each term
contributes 1
3 · 2 to that integral, giving a total of
3 · 2 =
12 > 3. If in this space we replace the
point x by a sequence of points x1, x2, . . . , such that d(xm, yi) = 1 + 1/m and d(xm, xn) = |1/m− 1/n|,
the above µ still has ||µ|| = 3, but the infimum defining that norm is not achieved.
7 Appendix: Translating convex sets to 0.
In §3, we saw that the radius of a subset X of a normed vector space V could be larger when measured
within a convex subset C of V than within the whole space V. If we regard this as a pathology, we would
like to know in which V it does not occur. We shall obtain partial results below, which, we will see, make
it likely that for n > 2, the only norms on Rn for which it does not happen are those giving a structure
isomorphic to En.
Observe that the radius of X, whether within V or within a convex subset C, is determined by the set
of closed balls containing X, and that these are all convex; hence that radius is a function of the convex hull
of X. So our question reduces to the case where X is convex. Moreover, if X shows the above behavior
with respect to one convex subset C of V, it will show it with respect to any smaller convex subset in
which it lies; these two observations reduce our question to the case where C = X. This reduction is the
equivalence of conditions (41) and (42) of the next lemma. Condition (43) then reformulates the problem.
(Note that in (43) and similar statements throughout this section, an expression such as “C − v” will
denote the translate of the set C by the vector −v, in contrast to notations such as X−{x} for set-theoretic
difference, used occasionally in earlier sections.)
Lemma 27. If V is a locally compact normed vector space, with closed unit ball B, then the following
conditions are equivalent:
(41) For every nonempty subset X of V, and convex subset C of V containing X, one has
radV (X) = radC(X).
(42) For every nonempty convex subset C of V, one has radV (C) = radC(C).
(43) Every nonempty closed convex subset C of B has a translate C − v which contains 0 and is
again contained in B.
Proof. We have noted the equivalence of (41) and (42); let us prove (42) equivalent to (43).
(43)⇒ (42): Dilating by arbitrary constants, we see that if (43) holds for B, then it holds for rB for
all positive real numbers r. Moreover, the statement that C − v contains 0 and is contained in rB is
equivalent to saying that v ∈ C and that v+ rB contains C; i.e., that C is contained in the ball of radius
r about v ∈ C. Thus (43) says that if a closed convex set C is contained in some closed ball about some
point of V (taken without loss of generality to be 0), then it is contained in a ball of the same radius about
one of its own points. This yields the case of (42) where C is closed. The facts that the convex hull of a
finite subset of V is compact, hence closed, and that the radius of an arbitrary set is the supremum of the
radii of its finite subsets, allow us to deduce the general case of (42) from the case of closed C.
(42)⇒ (43): If C is a closed convex subset of V contained in B, then radV (C) ≤ 1, so by (42),
radC(C) ≤ 1. Moreover, compactness of C implies that the set of radii of closed balls containing C and
centered at points v ∈ C achieves this minimum radC(C) ≤ 1, so that C is contained in a translate v+B
(v ∈ C), i.e., C − v ⊆ B.
Now (43) is a statement purely about the convex set B in the topological vector space V, so our question
becomes that of which subsets B of a topological vector space V satisfy it. (In the statement of the lemma,
the topology and the set B both arise from the normed structure on V ; but that relation is not needed by
the statement of (43) alone.) Here are some pieces of language, one ad hoc, the rest more or less familiar,
that we will use in examining this question.
Definition 28. If C ⊆ B are convex subsets of a real vector space V, with 0 ∈ B, we shall call C parkable
in B if there exists v ∈ C such that C − v ⊆ B. When clear from context, “in B” may be omitted.
If V is a real topological vector space, we will call sets of the form {x ∈ V | L(x) = a}, where L is a
nonzero continuous linear functional on V and a ∈ R, hyperplanes, while sets of the form {x ∈ V | L(x) ≥
a} will be called closed half-spaces.
A subset S of a vector space V will be called centrally symmetric if S = −S. A center of symmetry of
a subset S of V will mean a point v ∈ V such that S − v is centrally symmetric; equivalently, such that
S = 2v − S. (Note that a center of symmetry of a nonempty convex set belongs to that set.)
Lemma 29. Let B be a compact convex subset of Rn containing 0. Then the following conditions are
equivalent:
(44) The intersection of B with every hyperplane A that meets B is parkable.
(45) The intersection of B with every closed half-space H that meets B is parkable.
(46) Every nonempty closed convex subset C of B is parkable (= (43) above).
Proof. (46)⇒ (44) is clear; we will show (44)⇒ (45)⇒ (46).
(44)⇒ (45): Let H be a closed half-space in Rn, bounded by a hyperplane A, and meeting B. If H∩B
contains 0 it is trivially parkable, so assume the contrary. Thus B meets both H and its complement,
hence it meets their common boundary A, so by (44) there exists v ∈ A ∩ B such that (A ∩B) − v ⊆ B.
I claim that (H ∩ B) − v is also contained in B. Indeed, let p ∈ H ∩ B; we wish to show p − v ∈ B.
Intersecting our sets with the subspace of V spanned by p and v, and taking appropriate coordinates, we
may assume that n = 2, that A is the line {(x, y) | y = 1} ⊆ R2, and that v is the point (0, 1). H will
be the closed half-plane {(x, y) | y ≥ 1}, so we can write p = (xp, yp) with yp ≥ 1.
In this situation, A ∩ B will be a line segment (possibly degenerate) extending from a point (s, 1) to
a point (t, 1) (s ≤ t). Since (A ∩ B) − v ⊆ B, B also contains the segment from (s, 0) to (t, 0). Note
that if xp were > t, then the point where the line segment from p = (xp, yp) ∈ B to (t, 0) ∈ B meets A
would have x-coordinate > t, contradicting the assumption that A ∩ B terminates on the right at (t, 1);
so xp ≤ t. Similarly, xp ≥ s. Thus, xp ∈ [s, t], so (xp, 0) ∈ B. Hence p − v = (xp, yp−1) lies on the line
segment connecting p = (xp, yp) ∈ B with (xp, 0) ∈ B, hence lies in B, as claimed.
(45)⇒ (46): Suppose C is a nonempty closed convex subset of B which is not parkable. By compactness
of B, among the translates of C contained in B there is (at least) one that minimizes its distance to 0 in
the Euclidean norm on Rn; let us assume C itself has this property. Let p be the point of C nearest to
0 in that norm, and let A be the hyperplane passing through p and perpendicular (again in the Euclidean
norm) to p regarded as a vector. Then C will lie wholly in the half-space H bounded by A and not
containing 0. (For if we had q ∈ C not lying in H, then points close to p on the line segment from p to q
would be nearer to 0 than p is.) Assuming (45), H ∩B is parkable; say v ∈ H ∩B with (H ∩B)− v ⊆ B.
Since v ∈ H, if we write v as the sum ap+ q of a scalar multiple of p and a vector q perpendicular to p,
the coefficient a will be ≥ 1, and so in particular, positive. It follows that for sufficiently small positive c,
the point p − cv will be closer to 0 than p is; moreover, if we take such a c that is ≤ 1, (H ∩ B) − cv
will still be contained in B, since H ∩B and (H ∩B)− v are. Hence C − cv is contained in B, and has
a point p− cv which is closer to 0 than p is, contradicting our minimality assumption on C and p.
(In the above result, we could have replaced Rn by any real Hilbert space.)
Clearly, the closed Euclidean unit ball in Rn satisfies (44), and hence (45) and (46); hence since those
conditions are preserved by invertible linear transformations, so does the closed region enclosed by any
ellipsoid centered at 0. On the other hand, our example in the paragraph containing (23), of a normed
vector space in which (41) failed, had for its unit ball B a 3-cube centered at 0, showing that our properties
fail for that B. To see geometrically the failure of (44) for that B, choose a vertex of that cube and pass
a plane A through the three vertices adjacent thereto; it is not hard to see that A ∩ B is not parkable.
One can similarly show that none of the regular polyhedra centered at 0 satisfy (44), nor a circular cylinder
centered at 0, nor the solid obtained by attaching a hemisphere to the top and bottom of such a cylinder.
In fact, for n > 2, I know of no compact convex subset of Rn with nonempty interior that does satisfy that
condition, other than the regions enclosed by ellipsoids centered at 0. The situation is different for n = 2,
as shown by point (d) of the next result.
Lemma 30. Suppose B is a centrally symmetric convex subset of Rn. Then
(a) Any nonempty convex subset C ⊆ B that has a center of symmetry is parkable in B.
Hence, assuming in the remaining points that B is also compact, we have
(b) If the intersection of B with every hyperplane A that meets B has a center of symmetry, then B
satisfies the equivalent conditions (44)-(46).
In particular,
(c) If B is the closed region enclosed by an ellipsoid in Rn, then B satisfies (44)-(46), and
(d) If n = 2, then without further restrictions, B satisfies (44)-(46).
Proof. Let C be as in (a), with center of symmetry z ∈ C. Then for every x ∈ C, 2z − x ∈ C ⊆ B, so by
central symmetry of B, we have x− 2z ∈ B. Averaging x and x− 2z, we get x− z ∈ B. Thus C− z ⊆ B,
so C is parkable.
It follows that any B as in (b) satisfies (44), hence by Lemma 29, all of (44)-(46).
In the situation of (c), the intersection of B with a hyperplane A, if nonempty, is either a point or the
region enclosed by an ellipsoid in A, hence has a center of symmetry, while in (d) the intersection of B
with every line in R2 that meets B is a point or a closed line segment, hence has a center of symmetry; so
in each case, (b) gives the asserted conclusion.
Question 31. Let n ≥ 3, and suppose B is a compact convex subset of Rn having nonempty interior and
containing 0. Of the implications (i)⇒ (ii)⇒ (iii), which we have noted hold among the conditions listed
below, is either or both reversible?
(i) B is an ellipsoid centered at 0.
(ii) B is centrally symmetric, and for every hyperplane A meeting B, A ∩B has a center of symmetry.
(iii) Every closed convex subset of B is parkable in B.
Branko Grünbaum has pointed out to me a similarity between this question and the result of W.Blaschke
[3, pp.157–159] that if E is a smooth compact convex surface in R3 with everywhere nonzero Gaussian
curvature, such that when E is illuminated by parallel rays from any direction, the boundary curve of the
bright side lies in a plane, then E is an ellipsoid. I believe that methods similar to Blaschke’s may indeed
show that both implications of Question 31 are reversible. To see why, suppose B is a compact convex
subset of R3 with nonempty interior containing 0, which satisfies (iii) above, and whose boundary E is (as
in Blaschke’s result) a smooth surface with everywhere nonzero Gaussian curvature. Let A be any plane
through 0, and A′ the plane gotten by shifting A a small distance. Now the vectors that can possibly park
A′ ∩ B are constrained by the directions of the tangent planes to E at the points of A′ ∩ E (which are
well-defined because E is assumed smooth), and if we take A′ sufficiently close to A, these tangent planes
become close to the corresponding tangent planes at the points of A ∩ E. Applying the above observations
to planes A′ on both sides of A, one can deduce that all the tangent planes to E along A∩E must contain
vectors in some common direction (I am grateful to Bjorn Poonen for this precise formulation of a rough
idea I showed him); in other words, that A ∩ E is the boundary of the bright side when E is illuminated
by parallel rays from that direction. By definition, A∩E lies in the plane A; so we have the situation that
Blaschke considered, except that we have started with planarity and concluded that the curve is a boundary
of illumination, rather than vice versa.
That last difference is probably not too hard to overcome. More serious is the smoothness assumption
on E, used in both the above discussion and Blaschke’s argument. Finally, can the result be pushed from
n = 3 to arbitrary n ≥ 3 ? I leave it to those more skilled than I in the subject to see whether these ideas
can indeed be turned into a proof that (iii)⇒(i) in Question 31.
A related argument which can be extracted from a step in Blaschke’s development shows that a compact
convex subset B of R2 containing 0 and satisfying (46), whose boundary is a smooth curve containing
no line segments, must be centrally symmetric. Again, one would hope to remove the conditions on the
boundary.
One can ask about a converse to another of our observations:
Question 32. Suppose C is a compact convex subset of Rn (n > 2) such that for every centrally symmetric
compact convex subset B of Rn containing a translate C′ of C, the set C′ is parkable in B. Must C
have a center of symmetry?
Here the behavior of a given C can change depending on whether the dimension of the ambient vector
space is 2 or – as in the above question – larger: a triangle C has the above property in R2 by Lemma 30(d),
but not in R3, as we saw in the example where B was a cube.
Returning to the “pathology” which motivated the considerations of this section, one important case is
where the radius of a subset X of a normed vector space V decreases when V is embedded in a larger
normed vector space W. The next lemma determines how far down the radius of a given X can go.
Lemma 33. Let V be a normed vector space, and X a bounded subset of V. Then
(47) infW⊇V radW (X) = radV {(x− y)/2 | x, y ∈ X},
where W ranges over all normed vector spaces containing V. This infimum is realized by a W in which V
has codimension 1.
Proof. First consider any normed vector space W containing V, and suppose X is contained in the closed
ball of radius r about w ∈W. That ball has w as a center of symmetry, so it also contains {2w−y | y ∈ X},
hence taking midpoints of segments connecting that set to points x ∈ X, it contains {w+ (x− y)/2 | x, y ∈
X}. Translating by −w, we see that the ball of radius r about 0 contains {(x− y)/2 | x, y ∈ X}, so r is
at least the right-hand side of (47). This gives the inequality “≥ ” in (47); it remains to construct a W for
which radW (X) equals that right-hand side.
Before doing this, note that (47) holds trivially if X is empty or a singleton; so assuming it is neither,
let us re-scale and assume without loss of generality that the right-hand side of (47) equals 1. Since the set
whose radius is taken there is centrally symmetric, that set is contained in the closed unit ball BV of V.
(Cf. the proof of Lemma 30(a), which works not just for Rn, but for any normed vector space with B its
closed unit ball; or the proof of Lemma 5, applied to the 2-element group generated by x 7→ −x.) Now let
W = V ⊕ R, let us identify V with V × {0} ⊆ W, and let us take for the closed unit ball BW of W the
closure of the convex hull of
(48) {(x, 1) | x ∈ X} ∪ BV ∪ {(−x,−1) | x ∈ X}.
(We understand “closure” to mean “with respect to the product topology”, since we don’t have a norm until
we have made the above definition.) It is easy to see that any point in the convex hull of (48) whose second
coordinate is 0 is a convex linear combination of a point of BV and a member of the set on the right-hand
side of (47); but by assumption that set is contained in BV ; so in fact, BW ∩ V = BV , so the norm of W
indeed extends that of V.
But BW contains the translate {(x, 1) | x ∈ X} of X, hence X is contained in the closed ball of radius
1 about (0,−1), hence has radius ≤ 1 in W.
Even the case V = En is not immune to this phenomenon, since even in that case, the overspace W of
the above construction is generally not Euclidean. For instance, if we take for X an equilateral triangle in
2 centered at the origin, it is not hard to see that {(x− y)/2 | x, y ∈ X} is a hexagon whose vertices are
the midpoints of the edges a regular hexagon with the same circumcircle as X ; so the radius of X decreases
in W by the ratio of the inradius to the circumradius of a regular hexagon, in other words, by
This will not, of course, happen for a centrally symmetric X (cf. Lemma 30 or (47)). Other cases for
which it cannot happen depend on the metric structure: if X is a right or obtuse triangle in E2, or more
generally, any bounded set containing a diameter of a closed ball in which it lies, its radius clearly cannot
go down under extension of the ambient normed vector space (cf. Corollary 10).
8 Acknowledgements.
In addition to persons acknowledged above, I am indebted to W.Kahan for showing me an exercise he
had given his Putnam-preparation class, of proving Lemma 2 in E3, and for subsequently pointing out that
my solution to that exercise worked in any normed vector space; to Nik Weaver for information about his
results in [23], and to David Gale for pointing out the connection between the construction of §6 and results
in mathematical economics.
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George M. Bergman
Department of Mathematics
University of California
Berkeley, CA 94720-3840
gbergman@math.berkeley.edu
	The definition, and three examples.
	General properties of mapping radii.
	Some explicit mapping radii.
	Realizability of mapping radii.
	Related literature (and one more question).
	Appendix: The Arens-Eells space of X.
	Appendix: Translating convex sets to 0.
	Acknowledgements.

  It is known that every closed curve of length \leq 4 in R^n (n>0) can be
surrounded by a sphere of radius 1, and that this is the best bound. Letting S
denote the circle of circumference 4, with the arc-length metric, we here
express this fact by saying that the "mapping radius" of S in R^n is 1.
  Tools are developed for estimating the mapping radius of a metric space X in
a metric space Y. In particular, it is shown that for X a bounded metric space,
the supremum of the mapping radii of X in all convex subsets of normed metric
spaces is equal to the infimum of the sup norms of all convex linear
combinations of the functions d(x,-): X --> R (x\in X).
  Several explicit mapping radii are calculated, and open questions noted.

Mapping radii of metric spaces∗
George M. Bergman
November 4, 2018
Dedicated to the memory of David Gale
Abstract
It is known that every closed curve of length ≤ 4 in Rn (n > 0) can be surrounded by a sphere
of radius 1, and that this is the best bound. Letting S denote the circle of circumference 4, with the
arc-length metric, we here express this fact by saying that the mapping radius of S in Rn is 1.
Tools are developed for estimating the mapping radius of a metric space X in a metric space Y. In
particular, it is shown that for X a bounded metric space, the supremum of the mapping radii of X in
all convex subsets of normed metric spaces is equal to the infimum of the sup norms of all convex linear
combinations of the functions d(x,−) : X → R (x ∈ X).
Several explicit mapping radii are calculated, and open questions noted.
1 The definition, and three examples.
Definition 1. We will denote by Metr the category whose objects are metric spaces, and whose morphisms
are nonexpansive maps. That is, for metric spaces X and Y we let
(1) Metr(X,Y ) = {f : X → Y | (∀x0, x1 ∈ X) d(f(x0), f(x1)) ≤ d(x0, x1)}.
Throughout this note, a map of metric spaces will mean a morphism in Metr.
Given a nonempty subset A of a metric space Y, we define its radius by
(2) radY (A) = infy∈Y supa∈A d(a, y),
a nonnegative real number or +∞. For metric spaces X and Y, we define the mapping radius of X in Y
(3) map-rad(X,Y ) = supf∈Metr(X,Y ) radY (f(X))
= supf∈Metr(X,Y ) infy∈Y supx∈X d(f(x), y).
If X is a metric space and Y a class of metric spaces, we likewise define
(4) map-rad(X,Y) = supY ∈Y map-rad(X,Y )
= supY ∈Y, f∈Metr(X,Y ) infy∈Y supx∈X d(f(x), y).
(The term “mapping radius” occurs occasionally in complex analysis with an unrelated meaning [15, Def. 7.11].)
All vector spaces in this note will be over the field of real numbers unless the contrary is stated.
The result stated in the first sentence of the abstract has been discovered many times [5], [6], [18], [19],
[25]. (Usually, the length of the closed curve is given as 1 and the radius of the sphere as 1/4, but the
scaled-up version will be more convenient here.) Let us obtain it in somewhat greater generality.
∗2000 Mathematics Subject Classifications. Primary: 54E40. Secondary: 46B20, 46E15, 52A40.
Keywords: nonexpansive map between metric spaces, maximum radius of image, convex subset of a normed vector space.
Any updates, errata, related references etc., learned of after publication will be noted at
http://math.berkeley.edu/∼ gbergman/papers/ .
http://arxiv.org/abs/0704.0275v2
http://math.berkeley.edu/\protect 
Lemma 2. Let S denote the circle of circumference 4, with the arc-length metric. Then for any nonzero
normed vector space V, we have map-rad(S, V ) = 1.
Proof. In V, any 1-dimensional subspace U is isometric to the real line R, and we can map S into R, and
hence into U, by “folding it flat”, getting for image an interval of length 2. Since this interval has points at
distance 2 apart, its radius in V cannot be less than 1, so map-rad(S, V ) ≥ 1.
For the reverse inequality, consider any map f : S → V. We wish to find a point y ∈ V having distance
≤ 1 from every point of f(S). Let p and q be any two antipodal points of S, and let
y = (f(p) + f(q))/2.
Every point x ∈ S lies on a length-2 arc between p and q in S, hence d(p, x) + d(q, x) = 2, hence
d(f(p), f(x))+d(f(q), f(x)) ≤ 2, i.e., (d(f(p), f(x))+d(f(q), f(x)))/2 ≤ 1, so d((f(p)+f(q))/2, f(x)) ≤ 1,
as claimed.
Let us make explicit the argument used at the very last step above. It is the c1 = c2 = 1/2 case of
(5) If c1, . . . , cn are nonnegative real numbers summing to 1, and v1, . . . , vn are elements of a
normed vector space V, then for all w ∈ V, d(
ci vi, w) ≤
ci d(vi, w).
This can be seen by writing the left-hand side as ||(
civi) − w|| = ||
ci (vi − w)|| ≤
||ci (vi − w)|| =
ci d(vi, w).
Consider next the union X of two circles S0 and S1, each of circumference 4, intersecting in a pair of
points antipodal in each (e.g., take for S0 and S1 any two distinct great circles on a sphere of circumfer-
ence 4), again with the arc-length metric. We can show that this X also has mapping radius ≤ 1 in V by
the same argument as before, except that where we previously used an arbitrary pair of antipodal points,
we are now forced to use precisely the pair at which our circles intersect. We are not so restricted in the
example showing that radius 1 can actually be achieved – we can stretch one circle taut between any two
antipodal points, and for most choices of those points, we have a great deal of freedom as to what to do with
the other circle. In any case, we have
Lemma 3. Let X be the union of two circles S0 and S1, each of circumference 4, intersecting in a pair
of points antipodal in each, with the arc-length metric. Then for any nonzero normed vector space V, we
have map-rad(X,V ) = 1.
We could apply the same method to any number of circles joined at a common pair of antipodal points; but
let us move in a different direction. Again picturing S0 and S1 as great circles on a sphere of circumference 4
in Euclidean 3-space, assume they meet at right angles, and call their points of intersection the north and
south poles. Let us bring in a third circle, S2, the equator, and let X = S0 ∪ S1 ∪ S2, again with the arc
length metric.
We no longer have a pair of antipodal points belonging to all three circles; rather, we have three pairs of
points, S1 ∩ S2 = {p0, q0}, S2 ∩ S0 = {p1, q1}, and S0 ∩ S1 = {p2, q2}. Now given a normed vector space
V and a map f : X → V in Metr, suppose we let
(6) y = (f(p0) + f(q0) + f(p1) + f(q1) + f(p2) + f(q2))/6.
What can we conclude about d(y, f(x)) for x ∈ X ?
Say x ∈ S2. Since both {p0, q0}, and {p1, q1} are pairs of antipodal points of S2, we have d(p0, x) +
d(q0, x) = d(p1, x) + d(q1, x) = 2. The same will not be true of d(p2, x) and d(q2, x). To determine how
large these can get, let us take x ∈ S2 as far as possible (under our arclength metric) from the intersections
of S2 with our two circles through the poles p2 and q2. This happens when x is at the midpoint of any
of the quadrants into which p0, q0, p1 and q1 divide S2; in this situation, d(p2, x) = d(q2, x) = 3/2.
(Each quadrant has arc-length 1, and one has to go a quadrant and a half to get from p2 or q2 to x.) We
see, in fact, that for any x ∈ S2 we have d(p2, x) = d(q2, x) ≤ 3/2, hence d(p2, x) + d(q2, x) ≤ 3. Now
applying any map f : X → V, and invoking (5) with all ci = 1/6, we see that for y as in (6) we have
d(y, f(x)) ≤ (2 + 2 + 3)/6 = 7/6. We have proved this for x ∈ S2; by symmetry, it is also true for x lying
on S0 or S1. This allows us to conclude, not that map-rad(X,V ) = 1 as in the preceding two cases, but
(7) map-rad(X,V ) ≤ 7/6.
And in fact, there do exist maps f : X → V with radV (f(X)) > 1. To describe such a map, note that
X can be identified with the 1-skeleton of a regular octahedron of edge 1. In the next few paragraphs, let
us put aside our picture of X in terms of great circles on a sphere, and replace it with this (straight-edged)
octahedral skeleton.
If we look at our octahedron in Euclidean 3-space from a direction perpendicular to one of its faces, we see
that face and the opposite one as overlapping, oppositely oriented equilateral triangles, with vertices joined
by the remaining 6 edges, which look like a regular hexagon. Now suppose we regard these two opposite
triangular faces as made of stiff wire, and the other 6 edges as made of string. Then if we bring the planes of
the two wire triangles closer to one another, the string edges will loosen. Suppose, however, that we rotate
the top triangle clockwise as they approach one another, so that three of those strings are kept taut, while
the other three become still looser. When the planes of our wire triangles meet, those wire triangles will
coincide, and the three taut string edges will fall together with the three edges of that triangle, while the
three loose ones become loops, hanging from the three vertices. Let us lock the two wire triangles together,
and pull the three loops taut, radially away from the center of symmetry of the triangle.
What we then have is the image of a certain map f in Metr from our octahedral skeleton X into a
plane, which we can identify with R2. We see that radR2(f(X)) will be the distance from the center of
symmetry of our figure to each of the three points to which the drawn-out loops are stretched; i.e., the sum of
the distance from the center of symmetry to each vertex of the triangle, and the length of the stretched loop
attached thereto. The former distance is two thirds of the altitude of the triangle, (2/3)(
3/2) = 1/
and the latter length is 1/2 (since the loop doubles back), so
(8) radR2(f(X)) = 1/
3 + 1/2. Hence, map-rad(X,R2) ≥ 1/
3 + 1/2 > 1.
This shows that our three-circle space does indeed behave differently from the preceding one- and two-
circle examples.
However 1/
3 + 1/2 ≈ 1.0773, which falls well short of the upper bound 7/6 ≈ 1.1667 of (7).
We can overcome this deficiency by using a different norm on R2. Let V be R2 with the norm whose
unit disc is the region enclosed by a regular hexagon H of unit side. Note that the 6 sides of H are parallel
to the 6 radii joining 0 to the vertices of H, hence these sides have length 1 in the new metric, just as
in the Euclidean metric, and indeed, any line segment in one of those directions will have the same length
in both metrics. Now let us map X, still pictured as the 1-skeleton of a regular octahedron of side 1 in
Euclidean 3-space, into V so that, as before, two opposite triangles are embedded isometrically (now under
the metric of V ), and made to fall together with each other and with three of the other edges, while the
remaining three edges form loops that are stretched radially outward as far as they will go. Let us moreover
take the sides of our image-triangle to be parallel to three sides of H.
The map X → R2 that does this is almost the same one as before. The 9 edges that end up parallel to
edges of H are mapped exactly as before, since distances in those directions are the same in the two metrics.
The three folded loops end up set-theoretically smaller than before, since the new metric is greater in their
direction than is the Euclidean metric, and they go out a distance 1/2 in the new metric before turning back;
but they still contribute the value 1/2 to the calculation of the radius of our image of X. The significant
change in that calculation concerns the distance from the center of our triangle to its three vertices. Looking
at our triangle as a translate of one of the 6 equilateral triangles into which H is decomposed by its radii,
we see that the altitude of that triangle is equal to its side in this metric (since the midpoint of a side of H
has the same distance, 1, from the origin as a vertex of H does). Hence the distance from the center to a
vertex is 2/3. Adding to this the distance 1/2 from that vertex to the end of the loop attached to it, we
get 2/3 + 1/2 = 7/6. Assuming that the center of our triangle is indeed the minimizing point defining the
radius (i.e., is a value of y that yields the infimum (2); we will verify this in Lemma 5), this achieves the
upper bound (7). Summarizing, and making a few supplementary observations, we have
Lemma 4. Let X be the 1-skeleton of a regular octahedron of side 1, under the arc-length metric. Then
for any nonzero normed vector space V,
(9) 1 ≤ map-rad(X,V ) ≤ 7/6.
The exact value of map-rad(X,V ) is 1 if V is 1-dimensional, is ≥ 1/
3 + 1/2 if V is Rn (n ≥ 2)
under the Euclidean norm, and is 7/6 if V is R2 under the norm having for unit circle a regular hexagon.
Proof. The lower bound 1 in (9) is gotten as in the last full sentence before Lemma 3, by regarding X
as S0 ∪ S1 ∪ S2, straightening out one of these circles to cover a segment of length 2 in a 1-dimensional
subspace of V, and letting the other two circles collapse into that line in any way. (Or for a construction
that relies less on geometric intuition, pick any p ∈ S0, map X into R by the function d(p,−), note that
this map sends p and the point antipodal to p on S0 to 0 and 2, respectively, and embed R in V.) As
before, such an image of X has points 2 units apart, and so has radius ≥ 1 in V by the triangle inequality.
The upper bound 7/6 was obtained in (7).
To see that when V itself is 1-dimensional, the value 1 is not exceeded, note that the distance between
any two points of X is ≤ 2. Hence the image of X under any map into such a V is a segment of length
≤ 2, hence of radius ≤ 1.
The lower bounds 1/
3 + 1/2 and 7/6 for V = R2 with the two indicated norms were obtained above
by explicit mappings.
Let us now justify the assumption we made just before the statement of the above lemma, about the
center from which we computed the radius.
Lemma 5. Let V be a normed vector space, A a nonempty subset of V, and G a finite group of isometries
of V which preserve A. Then
(10) radV (A) = infy∈V G supa∈A d(a, y),
where V G is the fixed-point set of G.
In particular, if V G is a singleton {v0}, then
(11) radV (A) = supa∈A d(a, v0).
Proof. Given v ∈ V, let
(12) y = |G|−1
and note that this point lies in V G, and that for any a ∈ A,
(13) d(a, y) = d(a, |G|−1
gv) ≤
|G|−1d(a, gv) = |G|−1
d(g−1a, v)
≤ |G|−1
supb∈A d(b, v) = supb∈A d(b, v).
Here the first inequality holds by (5) and the second by considering b = g−1a. Hence for y ∈ V G defined
by (12), supa∈A d(a, y) ≤ supa∈A d(a, v), from which (10) follows. The final assertion is a special case.
For V = R2 with a regular hexagon as unit circle, the group G generated by a rotation by 2π/3 about
any point is an isometry of V, and if we take that point to be the center of symmetry of the set f(X) we
were looking at above, G preserves f(X) and has that center of symmetry as unique fixed point; so the
above lemma justifies our description of the radius of f(X) in terms of distance from that point. In the
earlier computation using the Euclidean metric on R2, we “saw” that the radius was measured from the
center of symmetry; this is now likewise justified by Lemma 5.
Lemma 4 leaves open
Question 6. For V = R2 under the Euclidean norm, and X the 1-skeleton of a regular octahedron of
side 1, where does map-rad(X,V ) lie within [1/
3 + 1/2, 7/6] ?
For V = Rn, again with the Euclidean norm, but n > 2, is the answer the same?
Having whetted our appetite with this example, let us prove some general results.
2 General properties of mapping radii.
Lemma 7. Let X, X ′, Y, Y ′ be nonempty metric spaces, Y and Y′ classes of such metric spaces, and
V and V ′ normed vector spaces.
(i) If there exists a surjective map h : X → X ′ (or more generally, a map X → X ′ with dense image) in
Metr, then map-rad(X ′, Y ) ≤ map-rad(X,Y ).
(ii) If Y ′ ⊆ Y, then for any nonempty subset A of Y ′ we have radY ′(A) ≥ radY (A). Here equality will
hold if Y ′ is a retract of Y ; i.e., if the inclusion of Y ′ in Y has a left inverse in Metr.
Hence if Y ′ is a retract of Y, then map-rad(X,Y ′) ≤ map-rad(X,Y ). In particular, this is true if Y
is a normed vector space (or more generally, a convex subset of such a space) and Y ′ the fixed subspace
(respectively, subset) of a finite group G of affine isometries of Y.
(iii) If Y′ ⊆ Y, then map-rad(X,Y′) ≤ map-rad(X,Y).
(iv) In contrast to (i) and (ii), for X ′ ⊆ X, either of the numbers map-rad(X ′, Y ) and map-rad(X,Y )
can be greater than the other, and if Y ′ ⊆ Y, or if Y ′ is a surjective image of Y in Metr, either of the
numbers map-rad(X,Y ′) and map-rad(X,Y ) can be greater than the other.
Proof. (i) Suppose h : X → X ′ has dense image. Then for any f : X ′ → Y, fh(X) is dense in f(X ′),
hence radY (fh(X)) = radY (f(X
′)), so the terms of the supremum defining map-rad(X,Y ) include all the
terms of the supremum defining map-rad(X ′, Y ), from which the asserted inequality follows.
(ii) The terms of the infimum defining radY (A) include the terms of the infimum defining radY ′(A),
giving the first inequality.
If there exists a retraction e of Y onto Y ′, then for every y ∈ Y and a ∈ A we have d(a, e(y)) ≤ d(a, y),
since e is nonexpansive and fixes points of A. Hence supa∈A d(a, e(y)) ≤ supa∈A d(a, y), and taking the
infimum of this over y ∈ Y, we get radY ′(A) ≤ radY (A). This and the previous inequality give the asserted
equality. Since Metr(X,Y ′) ⊆Metr(X,Y ), we also get map-rad(X,Y ′) ≤ map-rad(X,Y ), as claimed.
If Y is a convex subset of a normed vector space, and Y ′ the fixed set of a finite group G as in the final
assertion, note that the function e(v) = |G|−1
gv used in the proof of Lemma 5 is nonexpansive:
d(e(v), e(w)) = ||e(v)− e(w)|| = ||e(v − w)|| ≤ ||v − w|| = d(v, w),
and is a retraction of Y onto Y G.
(iii) This is again a case of suprema of a smaller and a larger set of real numbers.
(iv) The assertion for X ′ ⊆ X can be seen from the following mapping radii, where subsets of R are
given the induced metric:
map-rad({0}, {0, 2}) = 0, map-rad({0, 2}, {0, 2}) = 2, map-rad({0, 1, 2}, {0, 2}) = 0.
The assertion for Y ′ ⊆ Y is shown by the observations
map-rad({0, 2}, {0}) = 0, map-rad({0, 2}, {0, 2}) = 2, map-rad({0, 2}, {0, 1, 2}) = 1.
Finally, to get the case where Y ′ is a surjective image of Y, note that we have surjections {0, 3} → {0, 2} →
{0, 1} in Metr, and that
map-rad({0, 2}, {0, 3}) = 0, map-rad({0, 2}, {0, 2}) = 2, map-rad({0, 2}, {0, 1}) = 1.
(With a bit more work, one can construct sets X, Y0, Y1, Y2 ⊆ R such that each Yi+1 is both a subset and
a surjective image of Yi, and such that map-rad(X,Y0) < map-rad(X,Y1) > map-rad(X,Y2).)
To state consequences of the above results, let us fix some notation.
Definition 8. For n ≥ 0, n-dimensional Euclidean space, i.e., Rn with the Euclidean norm, will be denoted
n. The class of all Euclidean spaces, {En | n ≥ 0}, will be denoted Euc.
The class of all normed vector spaces, regarded as metric spaces, will be denoted NmV. The class of all
convex subsets of normed vector spaces, regarded as metric spaces, will be denoted Conv.
The diameter of a metric space X will be defined by diam(X) = supx,y∈X d(x, y).
Corollary 9. If X is a nonempty metric space, then
(14) map-rad(X,E1) ≤ map-rad(X,E2) ≤ . . . ≤ map-rad(X,En) ≤ . . . ,
with supremum map-rad(X,Euc). Further,
(15) diam(X)/2 = map-rad(X, E1) ≤ map-rad(X, Euc) ≤ map-rad(X, NmV)
≤ map-rad(X, Conv) ≤ map-rad(X, Metr) = radX(X) ≤ diam(X).
Proof. Since En is the fixed subspace of a reflection of En+1, the final assertion of Lemma 7(ii) gives (14).
(We could have put “0 = map-rad(X, E0) ≤” at the left end of (14); but this would complicate some
references we will want to make to (14) later.) By definition, map-rad(X,Euc) is the supremum of these
values.
To see the initial equality of (15), note on the one hand that under any nonexpansive map f : X → E1,
the images of any two points of X are ≤ diam(X) apart, hence f(X) must lie in an interval of length
≤ diam(X), and any interval in E1 has radius half its length, so map-rad(X, E1) ≤ diam(X)/2. On the
other hand, for x, y ∈ X, the function d(x,−) : X → E1 is nonexpansive, and the images of x and y under
this map are d(x, y) apart, whence the radius of f(X) is at least half this value. Taking the supremum
over all x and y, we get map-rad(X, E1) ≥ diam(X)/2.
The next four steps, inequalities among mapping radii, are instances of Lemma 7(iii). In the equality
following these, the direction “≤” simply says that nonexpansive maps are radius-nonincreasing, while “≥”
holds because one of the maps in the supremum defining map-rad(X, Metr) is the identity map of X. The
final inequality is immediate.
We note in passing some cases where these mapping radii are easy to evaluate.
Corollary 10. If a metric space X satisfies radX(X) = diam(X)/2, then all terms of (15) through
radX(X) are equal (and hence also equal to all terms of (14)).
In particular, this is true whenever (i) X is a finite tree, with edges of arbitrary positive lengths, under
the arc-length metric, or (ii) X has an isometry ρ with a fixed point 0 such that for every x ∈ X,
d(x, ρ(x)) = 2 d(x, 0).
Proof. The first sentence is clear from (15). To get the two classes of examples, it suffices to show in each
case that radX(X) ≤ diam(X)/2, since (15) gives the reverse inequality.
In case (i), X is compact, so we may choose x, y ∈ X with d(x, y) = diam(X). The unique non-self-
intersecting path between x and y is isometric to a closed interval, and so has a midpoint p, satisfying
d(x, p) = d(y, p) = diam(X)/2; it now suffices to show that d(z, p) ≤ diam(X)/2 for all z ∈ X. Consider
the unique non-self-intersecting path from p to z. Because X is a tree, that path out of p cannot have
nontrivial intersection with both the path from p to x and the path from p to y; assume it meets the
latter only in p. Then the unique non-self-intersecting path from z to y is the union of the path from z to
p and the path from p to y, and we know that it has length ≤ diam(X), so subtracting off diam(X)/2,
the length of the path from p to y, we conclude that the length of the path from z to p is ≤ diam(X)/2,
as required.
In case (ii), we have radX(X) ≤ supx∈X d(x, 0) = supx∈X d(x, ρ(x))/2 ≤ diam(X)/2.
Examples falling under case (ii) above include all centrally symmetric subsets of normed vector spaces
containing 0, under the induced metric, and a hemisphere under the geodesic metric.
A less trivial result, now. Recall that in proving the upper bounds on the mapping radii of Lemmas 2,
3 and 4, we in effect chose formal weighted combinations of points of X, and used these to specify convex
linear combinations of points of f(X) ⊆ V. We abstract this technique below. In the statement of the
theorem, as a convenient way to express formal weighted combinations of points of X, we use probability
measures on X with finite support. (Recall that a probability measure on X is a nonnegative-valued
measure µ such that µ(X) = 1, and that µ is said to have support in a set X0 if it is zero on every subset
of X −X0. Apologies for the double use of “d” below, for the distance function of the metric space and the
“d” of integration.)
Theorem 11. Let X be a nonempty metric space. Then
(16) map-rad(X,Conv) = infµ supx∈X
d(x, z) dµ(z),
where the infimum is over all probability measures µ on X with finite support.
Proof. We first prove “≤”, imitating the argument of Lemmas 2, 3 and 4. We must show, for any nonexpan-
sive map f : X → C, where C is a convex subset of a normed vector-space V, and any probability measure
µ on X with finite support, that
(17) radC(f(X)) ≤ supx∈X
d(x, z) dµ(z).
For any point x of X, let µx denote the probability measure on X with singleton support {x}.
Since the µ of (17) is a probability measure with finite support, it has the form c1µx1 + · · · + cnµxn ,
where x1, . . . , xn are points of X, and c1, . . . , cn are nonnegative real numbers summing to 1. The point
ci f(xi) lies in C, so by definition of the radius, the left-hand side of (17) is ≤ supx∈X d(y, f(x)) =
supx∈X d(
ci f(xi), f(x)), which by (5) is ≤ supx∈X
ci d(f(xi), f(x)), which, because f is nonex-
pansive, is ≤ supx∈X
ci d(x, xi). The sum in this expression is the integral in (17), giving the desired
inequality.
In proving the direction “≥” in (16), we may assume the metric space X is bounded, since otherwise
it has infinite diameter, in which case (15) tells us that the left hand side of (16) is infinite. Assuming
boundedness, we shall display a particular embedding e of X in a convex subset C of a normed vector
space U, such that radC(e(X)) is greater than or equal to the right-hand side of (16).
Let U be the space of all continuous bounded real-valued functions on X, under the sup norm, let
e : X → U take each x ∈ X to the function d(x,−) (this e is easily seen to be nonexpansive) and let C
be the convex hull of e(X). Now for x ∈ X, its image e(x) = d(x,−) can be written y 7→
d(y, z) dµx(z).
Hence an arbitrary u ∈ C, i.e., a convex linear combination of these functions, will have the same form, but
with µx replaced by a convex linear combination µ of the measures µx, i.e., a general probability measure
µ on X with finite support. For such a function u, and any x ∈ X, the distance d(e(x), u) in C is the
sup norm of u− e(x), which is at least the value of u− e(x) at x ∈ X, which is u(x)− 0 =
d(x, z) dµ(z).
The radius of e(X) in C is thus at least the infimum over all µ of the supremum over all x of this integral,
which is the right-hand side of (16).
Recall that when we obtained our bound (7) on the mapping radius of the 1-skeleton of an octahedron,
analogy and good luck led us to the formal linear combination of points of X used in (6) (in effect, a
probability measure µ), which turned out to give the optimal bound. In general we ask
Question 12. Let X be a finite graph with edges of possibly unequal lengths, under the arc-length metric.
Must there be a probability measure µ on X with finite support that realizes the infimum of (16)?
Is there an algorithm for finding such a µ if it exists, or if not, for evaluating (16)?
We cannot expect in general that a measure of the desired sort will have support in the set of vertices of
the graph X, as happened in Lemma 4. E.g., if X is isometric to a circle with arc-length metric, one can
show that a measure µ realizes the infimum of (16) if and only if it gives equal weight to p and q whenever
p and q are antipodal points; so if X is, say, an equilateral polygon with an odd number of vertices, µ
cannot be concentrated in the vertices.
A class of examples generalizing our octahedral skeleton, which it would be of interest to examine, are
the 1-skeleta of cross polytopes [7].
A situation simpler than that of Question 12 is that of a finite metric space X. Here the determination
of the right-hand side of (16) is a problem in linear programming; whether it has an elegant solution I don’t
know. The determination of map-rad(X,En) for such a space X is, similarly, in principle, a problem in
calculus.
In Corollary 10, we saw that the mapping radius is easy to compute for a space that has “a robust
center”. Using the preceding theorem, let us show the same for a space with a pair of “robust antipodes”.
Corollary 13. Suppose the metric space X has a pair of points p and q such that
(18) (∀ r ∈ X) d(p, r) + d(r, q) = d(p, q).
Then letting D = d(p, q), we have diam(X) = D, and map-rad(X,Conv) = D/2. Thus, the terms of (15)
through map-rad(X,Conv) are all equal to D/2.
In particular, this is true if X is the 1-skeleton of a regular tetrahedron or of a parallelopiped (in
particular, of a cube), with the arc-length metric, or is the 0-skeleton of any of the regular polyhedra other
than the tetrahedron, with metric induced by the arc-length metric on the 1-skeleton of that polyhedron.
The property (18) is, of course, inherited by any subspace of X containing p and q.
Proof. For any two points r, r′ ∈ X, we have
2 d(r, r′) ≤ (d(r, p)+d(p, r′))+(d(r, q)+d(q, r′)) = (d(p, r)+d(r, q))+(d(p, r′)+d(r′, q)) = 2D,
so d(r, r′) ≤ D, whence diam(X) = D. Now let µ be the probability measure giving weight 1/2 to each
of p and q. For this µ, the integral on the right-hand side of (16) has value D/2 for all x, hence the
supremum of that integral over x is D/2, hence (16) shows that map-rad(X,Conv) ≤ D/2. Comparing
with the first term of (15), we see that all the the terms of (15) through map-rad(X,Conv) (though not,
as before, through map-rad(X,Metr)) are equal.
For X the 1-skeleton of a regular tetrahedron, we get (18) on taking for p and q the midpoints of
two opposite edges. For X the 1-skeleton of a parallelopiped, we can use any two antipodal points (not
necessarily vertices. In picturing this case, it may help to note that X is isometric to the 1-skeleton of a
rectangular parallelopiped.) In the 0-skeleton cases, we use any pair of opposite vertices. In each case, the
verification of (18) is not hard.
The final sentence is clear.
So, for instance, for the 1-skeleta of the tetrahedron and cube of edge 1, the 6-tuples of terms of (15) (not
distinguishing terms shown connected by equals-signs) are (1, 1, 1, 1, 3/2, 2) and (3/2, 3/2, 3/2, 3/2, 3, 3)
respectively. (The reason the last two numbers are equal for the cube, but distinct for the tetrahedron, is
that for the cube, the function x 7→ supy d(x, y) is 3 for all x, while for the tetrahedron, it ranges from
a maximum value 2 at the midpoints of the edges to a minimum value 3/2 at the vertices. In neither of
these cases is the maximum twice the minimum, so neither of them falls under Corollary 10.)
Let us note a curious feature of the construction used in Theorem 11: it has what at first looks like a
universal property (part (i) of the next result) but turns out not to be (part (ii)).
Corollary 14 (to proof of Theorem 11). Let X be a bounded metric space, let U be the space of continuous
bounded real-valued functions on X under the sup norm (cf. second half of the proof of Theorem 11), and
let e : X → U be the map taking each x ∈ X to the function d(x,−).
Now let f : X → V be any map (in Metr) from X into a normed vector space V. Then
(i) For every family of points x1, . . . , xn ∈ X, every family c1, . . . , cn of nonnegative real numbers summing
to 1, and every x ∈ X, one has
(19) d(f(x),
ci f(xi)) ≤ d(e(x),
ci e(xi)).
However,
(ii) Given points x1, . . . , xn ∈ X, and two families of nonnegative real numbers b1, . . . , bn and c1, . . . , cn,
each summing to 1, it is not necessarily true that
(20) d(
bi f(xi),
ci f(xi)) ≤ d(
bi e(xi),
ci e(xi)).
Thus, the convex hull of e(X) need not admit a map (in Metr) to the convex hull of f(X) making a
commuting triangle with e and f.
Proof. (i) may be seen by combining the calculations of the last sentence of the proof of the “≤” direction
of Theorem 11, which shows that d(f(x),
ci f(xi)) ≤
ci d(x, xi), and the end of the proof of the “≥”
direction, which, by evaluating e(xi) and e(x) as elements of the function-space U at the point x, shows
that d(e(x),
ci e(xi)) ≥
ci d(x, xi).
To get (ii), let X again be a circle of circumference 4 with arc-length metric, and let x0, x1, x2, x3 ∈ X
be four points equally spaced around it. Note that for any u ∈ X, we have d(x0, u) + d(x2, u) = 2 =
d(x1, u)+d(x3, u). Hence if we choose the bi and ci so that the right-hand-side of (20) is d((e(x0)+e(x2))/2,
(e(x1)+e(x3))/2), we see that this value is 0. On the other hand, if we map X into E
1 by f(x) =
1 − max(d(x0, x), 1), then of the f(xi), only f(x0) is nonzero, so the left-hand side is not 0, so (20)
fails.
There are, in fact, a different normed vector space U and mapping e : X → U for which the universal
property of (20) does hold [23, Theorem 2.2.4]; we examine this construction in an appendix, §6.
3 Some explicit mapping radii.
A classical result of H. E.W. Jung is, in effect, an evaluation of the mapping radius in En of a very simple
metric space.
Theorem 15 (after Jung [17]). Let D∞ denote an infinite metric space in which the distances between
distinct points are all 1. (The cardinality does not matter as long as it is infinite.) Then the values of
map-rad(D∞, E
n) for n = 0, 1, 2, . . . are, respectively,
(21) 0 < 1/2 < 1/
3/8 < . . . <
n/(2(n+ 1)) < . . . .
Hence, map-rad(D∞, Euc) = 1/
Likewise, for any positive integer m, if we let Dm be an m-element metric space with all pairwise
distances 1, then for every n ≥ 0,
(22) map-rad(Dm, E
r/(2(r + 1)) , where r = min(m−1, n).
Hence, map-rad(Dm, Euc) =
(m− 1)/(2m) .
Summary of proof. The main result of [17] is that every subset of En of diameter ≤ 1 has radius ≤
n/(2(n+ 1)) . This gives map-rad(D∞, E
n/(2(n+ 1)) . On the other hand, the n+ 1 vertices of
the n-simplex of edge 1 in En form a subset of radius exactly
n/(2(n+ 1)) , and clearly D∞ can be
mapped onto that set, establishing equality. Taking the limit of this increasing sequence as n → ∞, one
gets map-rad(D∞, Euc) = 1/
Clearly, the hypothesis m > n works as well as m =∞ in concluding as above that map-rad(Dm, En) =
n/(2(n+ 1)) . For m ≤ n, on the other hand, any image of Dm in En lies in an affine subspace that can be
identified with Em−1, so in that case we get map-rad(Dm, E
n) = map-rad(Dm, E
m−1) =
(m− 1)/(2m) .
Combining these results, we get (22) and the final conclusion.
The inequalities (21) show that each step of (14) can be strict. What about the steps of (15)? If we
identify terms connected by equal-signs, then (15) lists six possibly distinct values, connected by five ≤-signs.
Three of these ≤-signs are shown strict by the 3-point metric space D3 of the above theorem, for which,
I claim, the 6-tuple of values is (1/2, 1/
3 , 2/3, 2/3, 1, 1). The first of these values, and the last two,
are clear, and the second comes from the above theorem (line after (22)). To evaluate the remaining two
values, map-rad(D3, NmV) and map-rad(D3,Conv), consider the embedding e : D3 → U as in the last
paragraph of the proof of Theorem 11. The space U used there can in this case be described as R3 under
the sup norm; let C be the convex hull in U of
(23) e(D3) = {(0, 1, 1), (1, 0, 1), (1, 1, 0)}.
Then Lemma 7(ii) (in particular, the final sentence) tells us that radC(e(D3)) is the common distance of
the three points of e(D3) from the unique point of C invariant under cyclic permutation of the coordinates,
namely (2/3, 2/3, 2/3). This common distance is 2/3 (since each member of e(D3) has a zero coordinate),
so radC(e(D3)) = 2/3, and by Theorem 11, this is map-rad(D3,Conv). Since map-rad(D3, NmV) ≤
map-rad(D3, Conv), to show that map-rad(D3, NmV) is also 2/3 it will suffice to obtain a nonexpansive
map f of D3 into a vector space V such that radV (f(D3)) = 2/3. This may be done by using the same
mapping as above, but translated by (−2/3,−2/3,−2/3), so that the affine span of its image becomes a
vector subspace of R3, which, with its induced norm, we take as our V. The preceding argument now gives
radV (f(D3)) = 2/3.
For a space showing strict inequality at the final step of (15), radX(X) ≤ diam(X), one can use any
nontrivial instance of Corollary 10; for instance, the unit interval [0, 1], for which that corollary shows that
the 6-tuple in question is (1/2, 1/2, 1/2, 1/2, 1/2, 1).
This leaves the step
(24) map-rad(X,NmV) ≤ map-rad(X,Conv).
I thought at first that equality had to hold here: that for a C a convex subset of a normed vector space V
and any A ⊆ C (in particular, the image of any map of a metric space into C), one had radV (A) = radC(A).
However, this is not so: consider the untranslated case (23) of the above D3 example, and note that the point
(1/2, 1/2, 1/2) ∈ U has distance 1/2 from each point of (23); so radU (e(D3)) ≤ 1/2 < 2/3 = radC(e(D3)).
Nonetheless we have seen that for X = D3, equality holds in (24). Here, however, is an example (which
it took attempts spread over many months to find) for which that inequality is strict.
Consider the graph with 7 vertices, x, y0, y1, y2, z0, z1, z2, and 9 edges: a length-1 edge from x to each
of the yi, and a length-2 edge from yi to zj whenever i 6= j; and let X be the vertex-set of this graph,
with arc-length metric. Thus, for all i 6= j we have
(25) d(x, yi) = 1, d(x, zi) = 3, d(yi, yj) = 2,
d(yi, zj) = 2, d(yi, zi) = 4, d(zi, zj) = 4.
Let us first find map-rad(X,Conv), using Theorem 11. We must maximize the infimum (16) over the
convex linear combinations of µx, . . . , µz2 . By Lemma 5, it suffices to maximize that expression over points
invariant under permutations of the subscripts; i.e., over convex linear combinations of
(26) µx, µy = (µy1 + µy2 + µy3)/3, µz = (µz1 + µz2 + µz3)/3.
We find that
(27) µx(x) = 0, µx(yi) = 1, µx(zi) = 3,
µy(x) = 1, µy(yi) = 4/3, µy(zi) = 8/3,
µz(x) = 3, µz(yi) = 8/3, µz(zi) = 8/3.
Any convex linear combination of these three functions has value ≥ 8/3 at each zi; so every value of the
supremum in (16) is at least 8/3. Moreover, taking µ = µy (or more generally, µ = (1− t)µy + tµz for any
t ∈ [0, 5/6]), we see that this value 8/3 is attained; so
(28) map-rad(X,Conv) = 8/3.
The idea of our verification that map-rad(X,NmV) is strictly smaller than (28) will be to use the non-
convex affine combination (3µy − µx)/2 of the functions (27), so as to reduce somewhat the highest values
of µy, those at the zi, without bringing the values at other points up by too much. But since we don’t have
the analog of Theorem 11 for non-convex combinations (and indeed, that analog is not true in general – if it
were, then 2µy − µx would lead to a still better result, but it does not), we must calculate by hand rather
than calling on such a theorem. So suppose f is a nonexpansive map of X into a normed vector space V,
and let
(29) p = (f(y0) + f(y1) + f(y2)− f(x))/2.
We need to bound the distances between p and the points of f(X). In view of the symmetry of (29), it will
suffice to bound the distances to f(x), f(y0) and f(z0). We calculate
(30) d(p, f(x)) = || (f(y0) + f(y1) + f(y2)− f(x)− 2f(x))/2 ||
≤ (||f(y0)− f(x)||+ ||f(y1)− f(x)|| + ||f(y2)− f(x)||)/2 ≤ (1 + 1 + 1)/2 = 3/2.
d(p, f(y0)) = || (f(y0) + f(y1) + f(y2)− f(x)− 2f(y0))/2 ||
≤ (||f(y1)− f(y0)||+ ||f(y2)− f(x)||)/2 ≤ (2 + 1)/2 = 3/2.
d(p, f(z0)) = || (f(y0) + f(y1) + f(y2)− f(x)− 2f(z0))/2 ||
≤ (||f(y0)− f(x)||+ ||f(y1)− f(z0)||+ ||f(y2)− f(z0)||)/2 ≤ (1 + 2 + 2)/2 = 5/2.
Taking the maximum of these values, we get
(31) map-rad(X,NmV) ≤ 5/2 < 8/3 = map-rad(X,Conv),
a strict inequality, as claimed.
The above observations suggest the question: Which normed vector spaces V have the property that
the radius of every subset X of V is the same whether evaluated in V, or in an arbitrary convex subset of
V containing X ? This is examined in an appendix, §7.
The example of (23) showed that the radius of a subset of a normed vector space could change when one
passed to a larger normed vector space. Let us note a curious consequence.
Lemma 16. Let U be R3 under the sup norm, and U0 ⊆ U be {(a, b, c) ∈ U | a + b + c = 0}. Then
there is no isometric reflection U → U having U0 as its fixed subspace. In fact, no finite group of affine
isometries of any normed vector space W containing U has U0 as its fixed subspace.
Proof. Let W be any normed vector space containing U, and let f : D3 → U0 be given by f(x) =
e(x) − (2/3, 2/3, 2/3), for e as in the paragraph containing (23). The first sentence of Lemma 7(ii) gives
radW (f(D3)) ≤ radU (f(D3)), which we saw is < radU0(f(D3)). On the other hand, if W had a finite group
G of affine isometries with fixed subspace U0, then Lemma 5 would give radW (f(D3)) = radU0(f(D3)).
Returning to (14) and (15), let us for simplicity reduce the number of independent values by “normalizing”
to the case diam(X) = 2, and ask for more detailed information than those inequalities.
Question 17. Let X run over all metric spaces of diameter 2. What can one say about the geometry of
the resulting sets of sequences
(32) {(map-rad(X,E1), map-rad(X,E2), . . . , map-rad(X,En), . . . )} ⊆ RN,
(33) {(map-rad(X, Euc), map-rad(X, NmV), map-rad(X, Conv), map-rad(X, Metr))} ⊆ R4 ?
Can one describe them exactly? Are they convex; or do they become convex on replacing the entries by
their logarithms, or under some other natural change of coordinates?
If two successive terms of a member of (32) are equal, is the sequence constant from that point on?
Another family of questions, suggested by Theorem 15, is
Question 18. For n ≥ 2, what can one say about the set of nonnegative real numbers that can be written
map-rad(X,En) for finite metric spaces X in which all distances are integers?
Are all such real numbers “constructible”, i.e., obtainable from rational numbers by a finite sequence of
square roots and ring operations?
Is this set well-ordered for each n ? (It has a smallest element 0, and a next-to-smallest element 1/2.)
Does this set change if “finite metric spaces X . . . ” is weakened to “bounded metric spaces X . . . ”?
For m < n, can one assert any inclusion between the sets of mapping radii into Em, and into En ? Are
there values that occur as map-rad(X,Euc) for some X, but not as map-rad(X ′,En) for any X ′ and n ?
(E.g., can 1/
2 be written in the latter form?)
We end this section with an observation made in [12] for the spaces En, which in fact holds for closed
convex subsets of arbitrary finite-dimensional normed spaces.
Lemma 19 (cf. [12, Proposition 29, p.14, and second paragraph of p.46]). If C is a closed convex subset of
a normed vector space V of finite dimension n, and A a subset of C with > n elements, then radC(A) =
supA0 radC(A0), where A0 runs over the n+1-element subsets of A.
Proof. “≥” is clear; so it suffices to show that if for some real number r, each A0 is contained in a closed
ball of radius r centered at a point of C, then so is A. Now for each a ∈ A, the set of v ∈ C such that
a lies in the closed ball in C of radius r about v is the closed ball in C of radius r about a, hence a
compact convex subset of V. To say that a set A0 is contained in some closed ball of radius r centered
at a point of C is to say that the intersection of these sets, as a runs over A0, is nonempty. By Helly’s
Theorem ([14], [8]), if a family of compact convex subsets of Rn has the property that every system of n+1
members of this family has nonempty intersection, then so does the whole family; which in this case means
that all of A is contained in a ball of the indicated sort.
The above lemma does not imply the corresponding statement for mapping radii. For example, let
X = {x, y0, y1, y2}, where x has distance 1/2 from each of the yi, and these have distance 1 from each
other. The maximum of the mapping radii in E2 of 3-element subsets of X is map-rad({y0, y1, y2},E2) =
map-rad(D3,E
2) = 1/
3. But map-rad(X,E2) ≤ radX(X) = 1/2.
On the other hand, for this example, map-rad(X,E2) can be described as the infimum over p ∈ X of
the supremum of map-rad(X0,E
2) over all 3-element subsets X0 of X containing p. So we ask
Question 20. Does there exist, for every positive integer n, a positive integer N and a formula which
for every metric space X of ≥ N elements, and every normed vector space V of dimension n, expresses
map-rad(X,V ), using the operations of suprema and infima, in terms of the numbers map-rad(X0, V ), for
N -element subsets X0 ⊆ X ?
4 Realizability of mapping radii.
For a subset A of a metric space Y, let us say that radY (A) is realized if the infimum in the definition (2)
of that expression is attained, that is, if there exists y ∈ Y such that A is contained in the closed ball of
radius radY (A) about y.
Likewise, for metric spaces X and Y, let us say that map-rad(X,Y ) is realized if the supremum in the
definition of that expression is attained; that is, if there exists an f : X → Y such that radY (f(X)) =
map-rad(X,Y ). (This does not presume that radY (f(X)) is realized.)
Lemma 21. Let X and Y be nonempty metric spaces.
(i) If Y is compact, then for any subset A ⊆ Y, radY (A) is realized.
(ii) If X and Y are both compact, then map-rad(X,Y ) is realized.
However
(iii) For X compact and Y bounded and complete, or for X bounded and complete and Y compact,
map-rad(X,Y ) may fail to be realized.
Proof. (i) follows from the fact that for bounded A, supa∈A d(a, y) is a continuous function of y, hence
assumes a minimum on Y.
To get (ii), we note that Metr(X,Y ) is a closed subset of the function space Y X , which is compact
because Y is, so Metr(X,Y ) is compact in the function topology. We would like to say that the real-valued
map on this space given by f 7→ radY (f(X)) is continuous, and hence assumes a maximum. For general
X, this continuity does not hold, as will follow from the second statement of (iii); but I claim that it holds if
X is compact. For given f ∈Metr(X,Y ) and ε > 0, compactness allows us to cover X by finitely many
open balls of radius ε/3, say centered at x1, . . . , xn. Consider the neighborhood of f in Metr(X,Y ) given
U = {g ∈Metr(X,Y ) | d(f(xi), g(xi)) < ε/3 (i = 1, . . . , n)}.
Taking any x ∈ X and y ∈ Y, note that there exists i such that d(xi, x) < ε/3; hence for g ∈ U,
|d(f(x), y) − d(g(x), y)| ≤ d(f(x), g(x)) ≤ d(f(x), f(xi)) + d(f(xi), g(xi)) + d(g(xi), g(x))
≤ ε/3 + ε/3 + ε/3 = ε.
Thus, the two functions associating to every y ∈ Y the numbers supx∈X d(f(x), y) and supx∈X d(g(x), y)
differ everywhere by ≤ ε, whence the infima of these functions, radY (f(X)) and radY (g(X)) differ by ≤ ε,
giving continuity of f 7→ radY (f(X)), which, as noted above, yields (ii).
(iii) For an example with X but not Y compact, let X = D2, i.e., a space consisting of two points
at distance 1 apart, and let Y = {y2, y3, . . . , yn, . . . } ∪ {z}, with d(ym, yn) = 1 (m 6= n) and d(yn, z) =
1 − 1/n. Note that the radius in Y of a point-pair {z, yn} or {ym, yn} with m < n is 1 − 1/n. Now
Metr(X,Y ) consists of all set-maps X → Y, and it follows from the above calculation that map-rad(X,Y ) =
supn(1− 1/n) = 1, but that this value is not achieved. (If we had not specified that Y should be complete,
we could have used the simpler example, X = D2, Y = [0, 1).)
For an example with Y but not X compact, let X = {x2, x3, . . . , xn, . . . }, with d(x2n, x2n+1) = 2−1/n,
and all other pairs of distinct points having distance 1; and let Y = [0, 2] ⊆ E1. Note that if a map X → Y
is to have radius > 1/2, it must send some pair of points to values differing by > 1, and by our metric
on X, these two points must have the forms x2n, x2n+1. Since all other points have distance 1 from these
two, the images of all other points must fall within the interval between their images. Hence the image of
our map falls within an interval of length ≤ 2− 1/n for some positive n, i.e., of length < 2, and hence of
radius < 1. But such images can have radii arbitrarily close to 1, again giving a mapping radius that is not
realized.
Corollary 22. Suppose Y is a metric space in which every closed bounded subset is compact. Then
(i) For every bounded nonempty subset A ⊆ Y, radY (A) is realized.
(ii) If the isometry group of Y is transitive, or more generally, if Y has a bounded subset which meets
every orbit of that group, then for every compact nonempty metric space X, map-rad(X,Y ) is realized.
Proof. (i) Let radY (A) = r, choose any a0 ∈ A, and let Y ′ be the closed ball of any radius r′ > diam(A) ≥
r about a0 in Y. By assumption Y
′ is compact. We see that A ⊆ Y ′, and that every point y ∈ Y with
supa∈A d(a, y) ≤ r′ lies in Y ′. Since infy∈Y supa∈A d(a, y) = r, the space Y contains points y for which
supa∈A d(a, y) comes arbitrarily close to r; hence it will contain points for which that value is arbitrarily
close to r and is ≤ r′. Points with this latter property lie in Y ′, whence radY ′(A) is also equal to r, and
applying part (i) of the preceding lemma with Y ′ for Y gives the desired conclusion.
(ii) Suppose every orbit of the isometry group of Y meets the closed ball of radius c about y0 ∈ Y, and
let x0 be any point of X. Then every f : X → Y may be adjusted by an isometry of Y (which will preserve
the radius of f(X)) so that we get d(f(x0), y0) ≤ c, and after this adjustment, f(X) will lie in the closed
ball of radius c + diam(X) about y0. Letting Y
′ denote the closed ball of any radius r′ > c + diam(X)
about y0, we see as in the proof of (i) that the radii of these image sets f(X) in Y
′ will equal their radii
within Y, and applying part (ii) of the preceding lemma with Y ′ for Y, we get the desired conclusion.
5 Related literature (and one more question).
Lemma 2 above, determining the mapping radius of a circle in a normed vector space V, occurs frequently
in the literature (with E3 or En for V ) as an offshoot of the proof of Fenchel’s Theorem, the statement
that the total curvature of a closed curve C in E3 is at least 2π, with equality only when C is planar
and convex [9, Satz I]. To prove that theorem, Fenchel noted that this total curvature is the length of the
curve in the unit sphere S2 traced by the unit tangent vector to C, and that that curve cannot lie wholly
in an open hemisphere of S2 (nor in a closed hemisphere unless C is planar). He completed the proof by
showing [9, Satz I′ ] that a closed curve of length < 2π (respectively, equal to 2π) in S2 must lie in an
open hemisphere (respectively, must either lie in an open hemisphere or be a union of two great semicircles).
In our language, this says that a circle of circumference < 2π, made a metric space using arc-length, has
mapping radius < π/2 in S2 and (along with some additional information) that the circle of arc-length
exactly 2π has mapping radius π/2.
Subsequent authors [5], [6, Lemma on p.30], [16], [19], [21], [22] gave simpler proofs of Fenchel’s Satz I′
(similar to our proof of Lemma 2), and/or generalized that result from S2 to Sn, and/or obtained the more
precise result that the mapping radius of a circle of length L ≤ 2π in Sn is L/4, and/or noted that the
same method also gives the analogous result with En, or indeed any of a large class of geometric structures,
in place of Sn.
The last-mentioned generalizations were based on the observation that the concept of the midpoint of a
pair of points can be defined, and behaves nicely, in many geometric contexts. I do not know whether more
general convex linear combinations, such as we used in (5) and in the proof of Theorem 11, can be defined
outside the context of vector spaces so as to behave nicely; hence the emphasis in this note on vector spaces
and their convex subsets. A.Weinstein (personal communication) suggests that an approach to “averaging”
of points introduced by Cartan and developed further by Weinstein in [24] might serve this function. J.Lott
(personal communication) points similarly to the concepts of Hadamard space [2] and Busemann convex
space [4].
The results on closed curves of length L in the unit sphere cited above all take L ≤ 2π. If we write S1L
for a circle of circumference L with arc-length metric, and Sn for the unit n-sphere (of circumference 2π)
with geodesic distance as metric, it is clear that the result map-rad(S1L, S
n) = L/4 cannot be expected to
hold when L > 2π; but it would be interesting to investigate how that mapping radius does behave as a
function of L. For all L, map-rad(S1L, S
n) < π, since a curve of fixed length cannot come arbitrarily close
to every point of Sn, and if it misses the open disk of geodesic radius r about a point p, then it is contained
in the closed disc of geodesic radius π − r about the antipodal point.
Many of the papers referred to above consider arcs as well as closed curves; i.e., also study map-rad([0, L],
Sn), and prove that for L ≤ π, this equals L/2. Again, the case of larger L would be of interest. So we
Question 23. For fixed n > 1, how does map-rad(S1L, S
n) behave for L > 2π, and how does map-rad([0, L],
Sn) behave for L > π, as functions of L ?
For instance, are these two functions piecewise analytic?
It seems likely that there will be ranges of values of L in which different configurations of a closed curve or
arc give maximum radius, and that the value of this radius will be an analytic function of L within each such
range. (I conjecture that for all L between 2π and a value somewhat greater than 3π, map-rad(S1L, S
will be realized by a “3-peaked crown”, consisting of 6 arcs of great circles, with midpoints equally spaced
along a common equator. For map-rad(S1L, S
n) with n > 2, I have no guesses.)
Many papers in this area also consider the smallest “box” – in various senses – into which one can fit
all curves, or closed curves, of unit length [5] [13] [20], or all point-sets of unit diameter [10]. These do not
translate into statements about our concept of mapping radius for two reasons. First, they deal with arc
length in the Euclidean metric, but with “boxes” which, though they could in many cases be considered
closed balls in another metric, are not balls in the Euclidean metric; and our formalism of mapping radius
does not look at more than one metric on Y at a time. Second, they generally allow rotations as well as
translations in fitting the box around the curve, while in looking at radii we only have one closed ball of
each radius centered at a given point.
The intuitive interest of Question 23 above arises in part from a special property of the sphere: that a
large open or closed ball, i.e., one that falls just short of covering Sn, has for complement a small closed or
open ball. For spaces Y not having this property, the most natural analogs of those questions might be the
corresponding questions about “mapping co-radii”, given by the definitions
(34) coradY (A) = supy∈Y infa∈A d(a, y) (A ⊆ Y ),
(35) map-corad(X,Y ) = inff∈Metr(X,Y ) coradY (f(X))
= inff∈Metr(X,Y ) supy∈Y infx∈X d(f(x), y)
(cf. (2) and (3)). So, for instance, one might ask about the values of map-corad([0, L], B2) for B2 the closed
unit disc in R2, as a function of L.
(I’m not sure that “co-radius” is a good choice of term: one could argue that that term would more ap-
propriately apply either to radY (Y−A), or to what in the notation of (34) would be written coradY (Y −A).
So the above names are just suggestions, which others may choose to revise.)
6 Appendix: The Arens-Eells space of X.
At the end of §2, I mentioned that every metric space X admits an embedding in a normed vector
space U having the universal property that Corollary 14(ii) showed that the embedding we were considering
there did not have. The construction in question was introduced by Arens and Eells [1], and its universal
property noted by Weaver [23, Theorem 2.2.4], who calls it the Arens-Eells space of X. Weaver is there
most interested in this space as a pre-dual to the Banach space of Lipschitz functions on X. I will sketch
below a motivation for the same object in terms of the universal property. My description will also make a
couple of technical choices different from those of [1] and [23].
Essentially the same construction arises in mathematical economics, in the study of the “transportation
problem” [11], cf. [23, §2.3]. What to us will be the norm of an element of the Arens-Eells space appears
there as the minimum cost of transporting goods from a given set of sources to a given set of markets.
To lead up to the construction, let a metric space X be given, consider any map (as always, nonexpansive)
f of X into a normed vector space V, and let us ask, as a sample question: If we know the distances among
four points x1, x2, x3, x4 ∈ X, what can we say about ||f(x1) + f(x2)− f(x3)− f(x4)|| ?
Clearly, this will be bounded above by ||f(x1)−f(x3)|| + ||f(x2)−f(x4)|| ≤ d(x1, x3) + d(x2, x4). The
other way of pairing terms of opposite sign similarly gives the bound d(x1, x4) + d(x2, x3). Hence
(36) ||f(x1) + f(x2)− f(x3)− f(x4)|| ≤ min(d(x1, x3) + d(x2, x4), d(x1, x4) + d(x2, x3)).
For a similar, but slightly less straightforward case, suppose we want to bound ||3f(x1) + f(x2) −
2f(x3) − 2f(x4)||. We cannot, as before, pair off terms whose coefficients in this expression happen to be
the same except for sign. There are, however, ways of breaking up that expression as a linear combination
of differences; and a little experimentation shows that all ways of doing so are convex combinations of two
extreme decompositions. These two cases lead to the bound
(37) ||3f(x1) + f(x2)− 2f(x3)− 2f(x4)|| ≤
min(2d(x1, x3)+d(x1, x4)+d(x2, x4), 2d(x1, x4)+d(x1, x3)+d(x2, x3)).
We will not stop here to prove that (36) and (37) are best bounds. Let us simply observe that these
considerations suggest that the norm of such a linear combination of images of points of X under a universal
map e : X → U should be given by an infimum of linear combinations of the numbers d(x, y) (x, y ∈ X)
with nonnegative real coefficients, the infimum being taken over all such linear expressions which, when each
d(x, y) is replaced by e(x)− e(y), give the required element.
An obvious problem is that the only elements we get in this way are those in which the sum of the
coefficients of the members of e(X) is 0. This difficulty is intrinsic in the situation: There will not in fact
exist a nonexpansive map of X into a normed vector space having the standard sort of universal mapping
property with respect to such maps, because, though the condition of nonexpansivity bounds the distances
among images of points of X, it does not bound the distances between such images and 0; so universality
would force the images of points of X to have infinite norm.
What we can get, rather, is a set-map e of X into a vector space U, and a norm on the subspace U0
of linear combinations of images of points of X with coefficients summing to 0, such that for all x, y ∈ X,
||e(x) − e(y)|| ≤ d(x, y), and which has the universal property that given any nonexpansive map f of X
into a normed vector space V, there exists a unique vector-space homomorphism g : U → V which satisfies
f = ge, and is nonexpansive on U0. Observe that the norm on U0 induces a metric on each coset of that
subspace; in particular, on the coset U1 of elements in which the sum of all coefficients is 1, which is the
affine span of the image of X. The map of X into that coset is nonexpansive, and the asserted universal
property of e is easily seen to yield (20), the property that the construction of §2 failed to have.
Weaver’s answer to the same distance-to-0 problem is to use metric spaces with basepoint, and basepoint-
respecting maps, the basepoint of a vector space being 0. This has the advantage of giving a universal
property in the conventional sense, with both U and V in the category of normed vector spaces. However,
it requires one to make a possibly unnatural choice of basepoint in X ; changes in that choice induce isometries
on the universal space, which, though affine, are not linear. The approach I actually find most natural is
to regard what I have called U1 as a “normed affine space”, that is, a set with a simply transitive group
of “translation” maps by elements of a normed vector space, and to note that U1 has a genuine universal
property in the category of normed affine spaces. However, the development of that concept would be an
excessive excursion for this appendix. Still another approach would be to work with “normed” vector spaces
where the norm is allowed to take on the value +∞. In any case, it is straightforward to verify that the
Arens-Eells space of X as described in [23] and my U0 are isometrically isomorphic, so below I will quote
results of Weaver’s, tacitly restated for my version of the construction.
The details, now: let U be the vector space of all real-valued (i.e., not necessarily nonnegative) measures
µ on X with finite support, and, as before, for each x ∈ X let µx be the probability measure with support
{x}. Thus, {µx | x ∈ X} is a basis of U. Let U0 ⊆ U denote the subspace of measures µ satisfying
µ(X) = 0. Let W similarly denote the space of all real-valued measures on X × X with finite support;
for each (x, y) ∈ X ×X, let νx,y be the probability measure with support {(x, y)}, and let W+ ⊆ W be
the cone of nonnegative linear combinations of the νx,y, i.e., the nonnegative-valued measures on X ×X.
Finally, let D : W → U be the linear map defined by the condition
(38) D(νx,y) = µx − µy for x, y ∈ X,
which clearly has image U0. We now define the norm of any µ ∈ U0 by
(39) ||µ|| = inf
ν∈W+, D(ν)=µ
(x,y)∈X×X
d(x, y) dν .
It is easy to verify that this indeed gives a norm with the desired universal property. The one verification that
is not immediately obvious is that it is a norm rather than a pseudonorm; i.e., that it is nonzero for nonzero
µ ∈ U0. To get this, one first proves the desired universal property in the wider context of pseudonormed
vector spaces, then notes that given any nonzero µ =
ai µxi ∈ U0 (I finite, all ai nonzero), one can
find a nonexpansive map f : X → R which is zero at all but one of the xi, say xi0 , from which it follows
by the universal property that ||µ|| ≥ |
ai f(xi)| = |ai0 f(xi0)| > 0.
Weaver [23, Theorem 2.3.7(b)] shows that the infimum in (39) is always attained, and in fact, by a ν
whose “support” in X (the set of points which appear as x or y in terms νx,y having nonzero coefficient
in the expression for ν) coincides with the support of µ (the set of x such that µx appears with nonzero
coefficient in the expression for µ). Our next proposition strengthens this result a bit. For brevity, we will
call on Weaver’s result in the proof, but I will sketch afterward how the argument can be made self-contained.
We will use the following notation and terminology. Given ν =
bj νxj ,yj ∈ W (where J is a finite
set, the pairs (xj , yj) for j ∈ J are distinct, and all bj 6= 0), let Γ(ν) be the directed graph having for
vertices all points of X, and for directed edges the finitely many pairs (xj , yj) (j ∈ J). Let us define the
positive support of a directed graph Γ as the set of vertices which are initial points of its edges, and its negative
support as the set of vertices which are terminal points. For ν ∈ W, we will call the positive and negative
supports of Γ(ν) the positive and negative supports of ν. On the other hand, for µ =
ai µxi ∈ U0, let
us define its positive support to be {xi | ai > 0}, and its negative support to be {xi | ai < 0}. These are
clearly disjoint. Note that when ν ∈W+, the positive support of D(ν) is contained in the positive support
of ν, and contains all elements thereof that are not also in the negative support of ν, and that the negative
support of D(ν) has the dual properties.
When we speak of a cycle in a directed graph, we shall mean a cycle in the corresponding undirected
graph; we shall also understand that in a cycle no vertex is traversed more than once. Note that a cycle
of length 1 in Γ(ν) can only arise when a term νx,x has nonzero coefficient in ν, while a cycle of length
2, i.e., the presence of two edges between x and y, can only occur if νx,y and νy,x both have nonzero
coefficients. But a cycle of length n > 2 involving a given sequence of vertices may arise in any of 2n ways,
depending on the orientations of the edges.
We now prove
Proposition 24 (cf. [11, Theorem 3.3, p.84]). Let µ ∈ U0. Then the infimum of (39) is attained by an
element ν ∈W+ (not necessarily unique) whose positive and negative supports coincide respectively with the
positive and negative supports of µ, and whose graph Γ(ν) has no cycles.
Proof. As mentioned, Weaver proves the existence of a ν ∈ W+ with D(ν) = µ which achieves the infi-
mum (39) and has the same support as µ. Let ν be chosen, first, to have these properties; second, among
such elements, to minimize the total number of edges in Γ(ν), and finally, to minimize the sum of the
coefficients of all the νx,y in its expression. This last condition is achievable because the set of elements of
W+ which are linear combinations of a given finite family of the νx,y, and for which the coefficients of these
elements are all ≤ some constant, is compact; so after finding some ν with D(ν) = µ which achieves the
minimum (39), has the same support as µ, and minimizes the number of edges in Γ(ν), we may restrict our
search for elements also minimizing the coefficient-sum to the compact set of elements having these properties
and having every coefficient less than or equal to the coefficient-sum of the element we have found.
Suppose, now, that Γ(ν) has a cycle. Thus, we may choose distinct vertices p1, . . . , pk, and for each
j ∈ {1, . . . , k}, a term νpj ,pj+1 or νpj+1,pj occurring with positive coefficient in ν, where the subscripts j
are taken modulo k. (If k > 2 and both νpj ,pj+1 and νpj+1,pj occur in ν, we choose one of these arbitrarily.
If k = 2, we make sure that the terms we choose for j = 1, 2 are distinct, one being νp1,p2 and the other
νp2,p1 .) For each j ∈ {1, . . . , k} let us now define ν′pj ,pj+1 to be νpj ,pj+1 if that is the jth term in the
list we have chosen, or −νpj+1,pj if the jth term in that list is νpj+1,pj , and let ν′ =
ν′pj ,pj+1 ∈ W.
In general, ν′ /∈ W+, but for all λ ∈ R near enough to 0, we have ν + λν′ ∈ W+, since the relevant
coefficients in ν are strictly positive. Note that for each j, D(ν′pj ,pj+1) = µj − µj+1, hence D(ν
′) = 0,
hence D(ν + λν′) = D(ν) = µ.
Clearly,
(x,y)∈X×X
d(x, y) d(ν+λν′) is an affine function of λ. Hence it must be constant, otherwise,
using small λ of appropriate sign, we would get a contradiction to the assumption that ν achieves the
minimum of (39); so all the elements ν+λν′ achieve this same minimum. Now some choice of λ will cause
λν′ to exactly cancel the smallest among the coefficients of terms νpj ,pj+1 or νpj+1,pj in our cycle in Γ(ν).
Thus, ν + λν′ contradicts the minimality assumption on the number of edges in Γ(ν). This contradiction
shows that Γ(ν) has no cycles.
Next, let us compare the positive and negative supports of ν with those of µ. We have chosen ν so
that its support, namely the union of its positive and negative supports, coincides with the support of µ;
and since µ = D(ν), the positive and negative supports of ν will each contain the corresponding support
of µ. So if these inclusions are not both equalities, we must have a vertex p which is both in the positive
and the negative support of ν; i.e., such that there is an edge (q, p) of Γ(ν) leading into p, and an
edge (p, r) leading out of it. Let ν′ = νq,r − νq,p − νp,r. Like the element denoted by that symbol in the
preceding argument, this satisfies D(ν′) = 0. Let us again form ν + λ ν′, this time choosing the value
λ > 0 which leads to the cancellation of the smaller of the coefficients of νq,p and νp,r in ν, or of both
if these coefficients are equal. Since this does not reverse the sign of either of these coefficients, ν + λ ν′
still belongs to W+. Note that
(x,y)∈X×X
d(x, y) d(ν+λν′) ≤
(x,y)∈X×X
d(x, y) dν, since by the triangle
inequality, d(q, r) ≤ d(q, p) + d(p, r); so the property of minimizing the latter integral among elements of
W+ mapped to µ by D has not been lost. Also, Γ(ν + λν
′) has dropped at least one edge that belonged
to Γ(ν), since at least one coefficient was canceled, and has gained at most one edge, namely (q, r) (if that
was not previously present); so the total number of edges has not increased. Finally, when we look at the
sum of all the coefficients, we see that the coefficient of νq,r has increased by λ, while those of νq,p and
νp,r have both decreased by λ, so there has been a net change of −λ < 0. Thus, we have a contradiction to
our choice of ν as minimizing that sum. This completes the proof of the main assertion of the proposition.
Let us verify, finally, the parenthetical comment that the ν of the proposition may not be unique. Let
X be a 4-point space {x1, x2, x3, x4} where the distance between every pair of distinct points is 1, and let
µ = µx1 + µx2 − µx3 − µx4 . It is not hard to check that in this case, the only elements ν that can possibly
satisfy the conditions of the proposition are νx1,x3 + νx2,x4 and νx1,x4 + νx2,x3 . Since these give the same
value for the integral of (39), d(x1, x3) + d(x2, x4) = 2 = d(x1, x4) + d(x2, x3), each satisfies our conditions.
(Of course, for most choices of metric on this set X, one of these two values is smaller than the other,
and we then get a unique ν satisfying the conditions of the proposition.)
To get a self-contained version of the above proof which includes the existence result we cited from [23],
one may start by looking at any finite subset X0 of X containing the support of µ, verify by compactness
as above that the infimum of (39) over elements ν with support contained in X0 is achieved, then note
that any element in the support of ν but not in the support of µ must belong to both the positive and
negative supports of ν, a situation excluded by the proof. Letting X0 then run over all finite subsets of
X containing the support of µ, one sees that the infimum of (39) exists, and is simply the infimum with ν
restricted to have support in the support of µ.
We remark that the final condition of the above proposition, that Γ(ν) have no cycles, is not entailed
by the other conditions. E.g., returning to X = {x1, x2, x3, x4} with all distances 1, we see that every
convex linear combination ν of the two elements that we found, νx1,x3 + νx2,x4 and νx1,x4 + νx2,x3 , still
minimizes (39), and still has support X ; but if ν is a proper convex combination of those two elements,
then Γ(ν) = Γ(νx1,x3+ νx2,x4) ∪ Γ(νx1,x4+ νx2,x3), which contains (indeed, is) a cycle.
Let us now show, however, that when, as in the statement of the proposition, Γ(ν) is cycle-free, it
uniquely determines ν. Thus, the calculation of the norm (39) reduces in principle to checking finitely
many ν.
Lemma 25. Let µ ∈ U0, and let Γ be a directed graph with vertex-set X and without cycles. Then there
is at most one ν ∈W (and so, a fortiori, at most one ν ∈ W+) such that D(ν) = µ and Γ(ν) ⊆ Γ.
To characterize this element ν, consider any edge (x, y) in Γ. Let Γx (containing x) and Γy (containing
y) be the connected components into which the connected component of Γ containing (x, y) separates when
that edge is removed. Then the coefficient in ν of νx,y is the common value of
dµ and −
dµ, i.e.,
is both the sum of the coefficients of µz over z in Γx, and the negative of the corresponding sum over Γy.
Proof. We will prove the assertion of the second paragraph, from which that of the first clearly follows.
Writing µ = D(ν), the contributions to the expression
dµ from any term νp,q such that both p and
q lie in Γx clearly cancel, while terms such that neither p nor q lies in Γx contribute nothing. This leaves
the νx,y term, which contributes precisely its coefficient, leading to the first description of that coefficient.
Likewise, this term contributes the negative of its coefficient to
dµ, yielding the second description.
Corollary 26. Suppose µ ∈ U0 is integer-valued, and let ν be an element of W+ with the properties that
D(ν) = µ, that ν has the same positive and negative supports as µ, and that Γ(ν) has no cycles.
Then for every x ∈ X such that the coefficient of µx in µ is ±1, the vertex x is a leaf of Γ(ν).
Hence, if µ has the property that the coefficient of every µx is ±1, then ν is induced, in the obvious
way, by a bijection between the positive support of µ and the negative support of µ. Thus, in that case,
letting n be the common cardinality of these supports, there are exactly n! such ν ∈ W+.
Proof. Consider any x such that µx has coefficient +1 in µ. Then x is in the positive support of Γ(ν),
but not in the negative support. The latter condition says that ν involves no terms νy,x (y ∈ X), so +1
is the sum of the coefficients in ν of the terms νx,y (y ∈ X − {x}). Since ν ∈ W+, these coefficients are
nonnegative, and by Lemma 25 they are integers, so as they sum to 1, only one of them can be nonzero,
making x a leaf. The same argument, mutatis mutandis, gives the case where the coefficient of µx is −1.
The assertion of the final paragraph clearly follows, since a directed graph in which every vertex is a leaf
corresponds to a bijection between “source” and “sink” vertices.
As sample applications, recall the two computations at the beginning of this section, with which we
motivated the construction of our universal embedding e : X → U0. In our present notation, what we
were doing was evaluating the norms in U0 of elements of the two forms µx1 + µx2 − µx3 − µx4 and
3µx1 + µx2 − 2µx3 − 2µx4. In the first case, the last paragraph of the above corollary leads to just two
graphs, and hence two values of ν one of which must achieve the infimum (39), namely νx1,x3 + νx2,x4 and
νx1,x4 + νx2,x3 , showing that if f is our universal map e, equality holds in (36), and for general f, (36) is
the best bound. (This also establishes the example that we said was “not hard to check” in the next-to-last
paragraph of the proof of Proposition 24.)
In the case µ = 3µx1 + µx2 − 2µx3 − 2µx4, Corollary 26 says that x2 is a leaf of Γ(ν). As it lies in the
positive support of µ, the vertex it is attached to must lie in the negative support, i.e., must be either x3
or x4. In the former case, subtracting νx2,x3 from ν will give an element ν
′ ∈ W+ which is sent by D to
3µx1 − µx3 − 2µx4 . Since this ν′ has only one element, x1, in its positive support, its graph is uniquely
determined, giving ν′ = νx1,x3 +2νx1,x4 , hence ν = νx2,x3 + νx1,x3 +2νx1,x4 . The case where x2 is attached
to x4 similarly gives ν = νx2,x4 + 2νx1,x3 + νx1,x4 , and these together show that (37) is a best bound.
I referred earlier to the mathematical economist’s “transportation problem”. There, our d(x, y) corre-
sponds to the cost of transporting a unit quantity of goods from location x to location y; so our definition of
||µ|| describes the minimum cost of transporting goods produced and consumed at locations and in quantities
specified by µ.
Incidentally, the first assertion of Corollary 26 does not remain true if we weaken “the coefficient of µx
in µ is ±1” to “the coefficient of µx has least absolute value among the nonzero coefficients occurring in
µ.” For instance, suppose µ has the form 3µx1 − 4µx2 + 2µx3 − 4µx4 + 3µx5 . Then one of the elements of
W+ satisfying the conditions of Corollary 26 is ν = 3νx1,x2 + νx3,x2 + νx3,x4 + 3νx5,x4 . Here Γ(ν) has the
form x1 → x2 ← x3 → x4 ← x5, so x3, despite having smallest coefficient in µ, is not a leaf.
Proposition 24 sheds some light on our earlier “partial universality” result, Corollary 14(i). Given any
convex linear combination of points of our universal image of X,
aiµxi (ai > 0,
ai = 1), and any
point x ∈ X (which for simplicity we will assume is not one of the xi, though the argument can be adjusted
to the case where it is), the difference µx −
aiµxi is an element of U0 with positive support {x} and
negative support {xi | i ∈ I}. For this situation, the conditions of Proposition 24 clearly lead to a unique
Γ(ν), to wit, the tree whose edges are all pairs (x, xi) (i ∈ I), and hence to the unique choice ν =
ai νx,xi .
Thus, the right-hand side of (39) comes to
ai d(x, xi), which is equal to the right-hand side of (19). In
contrast, when one considers the difference between two general convex linear combinations of elements µx,
as in Corollary 14(ii), there may be many directed graphs satisfying the conditions of Proposition 24, so the
norm of that difference doesn’t have a simple expression.
The universality of the Arens-Eells space U yields a formula for map-rad(X,NmV) analogous to our
description (16) of map-rad(X,Conv); namely,
(40) map-rad(X,NmV) = infµ∈U1 supx∈X infν∈W+, D(ν)=µ−µx
y,z∈X
d(y, z) dν(y, z).
But this is cumbersome to use. E.g., the reader might try working through a verification, for the space
described by (25), of the statement that the µ implicit in (29), (µy0 + µy1 + µy2 − µx)/2, does indeed lead
to the infimum of (40), showing that map-rad(X,NmV) = 5/2, and not a smaller value.
Given an element µ ∈ U0, it would be interesting to look for bounds on the number of distinct graphs
Γ(ν) corresponding to elements ν ∈W+ as in the first sentence of Corollary 26. (This is simply a function
of the coefficients occurring in µ, as a family of positive real numbers with multiplicities.) To start with,
one might look for bounds in terms of the cardinalities of the positive and negative supports of µ.
Weaver [23] also gets a description of the universal nonexpanding map of X into a normed complex
vector space, paralleling the description for the real case, but he notes [23, p.43, next-to-last paragraph of
§2.2] that in the complex case it is no longer true that the infimum corresponding to (39) is always attained
by a ν having the same support as µ. (In the complex version of (39), by the way, one must replace dν by
|dν|, instead of restricting ν to a “positive cone” as above, since there is no natural analog of that cone.
Weaver takes this approach for both the real and complex cases; my use of W+ for the real case is one of
the different technical choices that I have made.) For instance, if X = {x, y0, y1, y2} with d(x, yi) = 1 and
d(yi, yj) = 2 (i 6= j), and if µ = µy0 + ωµy1 + ω2µy2 , where ω is a primitive cube root of unity, then the
minimizing ν is νy0,x + ωνy1,x + ω
2νy2,x, which makes that integral 3, while the best ν having support
in the support of µ, {y0, y1, y2}, is 13 (1− ω)νy0,y1 +
(ω − ω2)νy1,y2 + 13 (ω
2 − 1)νy2,y0 , of which each term
contributes 1
3 · 2 to that integral, giving a total of
3 · 2 =
12 > 3. If in this space we replace the
point x by a sequence of points x1, x2, . . . , such that d(xm, yi) = 1 + 1/m and d(xm, xn) = |1/m− 1/n|,
the above µ still has ||µ|| = 3, but the infimum defining that norm is not achieved.
7 Appendix: Translating convex sets to 0.
In §3, we saw that the radius of a subset X of a normed vector space V could be larger when measured
within a convex subset C of V than within the whole space V. If we regard this as a pathology, we would
like to know in which V it does not occur. We shall obtain partial results below, which, we will see, make
it likely that for n > 2, the only norms on Rn for which it does not happen are those giving a structure
isomorphic to En.
Observe that the radius of X, whether within V or within a convex subset C, is determined by the set
of closed balls containing X, and that these are all convex; hence that radius is a function of the convex hull
of X. So our question reduces to the case where X is convex. Moreover, if X shows the above behavior
with respect to one convex subset C of V, it will show it with respect to any smaller convex subset in
which it lies; these two observations reduce our question to the case where C = X. This reduction is the
equivalence of conditions (41) and (42) of the next lemma. Condition (43) then reformulates the problem.
(Note that in (43) and similar statements throughout this section, an expression such as “C − v” will
denote the translate of the set C by the vector −v, in contrast to notations such as X−{x} for set-theoretic
difference, used occasionally in earlier sections.)
Lemma 27. If V is a locally compact normed vector space, with closed unit ball B, then the following
conditions are equivalent:
(41) For every nonempty subset X of V, and convex subset C of V containing X, one has
radV (X) = radC(X).
(42) For every nonempty convex subset C of V, one has radV (C) = radC(C).
(43) Every nonempty closed convex subset C of B has a translate C − v which contains 0 and is
again contained in B.
Proof. We have noted the equivalence of (41) and (42); let us prove (42) equivalent to (43).
(43)⇒ (42): Dilating by arbitrary constants, we see that if (43) holds for B, then it holds for rB for
all positive real numbers r. Moreover, the statement that C − v contains 0 and is contained in rB is
equivalent to saying that v ∈ C and that v+ rB contains C; i.e., that C is contained in the ball of radius
r about v ∈ C. Thus (43) says that if a closed convex set C is contained in some closed ball about some
point of V (taken without loss of generality to be 0), then it is contained in a ball of the same radius about
one of its own points. This yields the case of (42) where C is closed. The facts that the convex hull of a
finite subset of V is compact, hence closed, and that the radius of an arbitrary set is the supremum of the
radii of its finite subsets, allow us to deduce the general case of (42) from the case of closed C.
(42)⇒ (43): If C is a closed convex subset of V contained in B, then radV (C) ≤ 1, so by (42),
radC(C) ≤ 1. Moreover, compactness of C implies that the set of radii of closed balls containing C and
centered at points v ∈ C achieves this minimum radC(C) ≤ 1, so that C is contained in a translate v+B
(v ∈ C), i.e., C − v ⊆ B.
Now (43) is a statement purely about the convex set B in the topological vector space V, so our question
becomes that of which subsets B of a topological vector space V satisfy it. (In the statement of the lemma,
the topology and the set B both arise from the normed structure on V ; but that relation is not needed by
the statement of (43) alone.) Here are some pieces of language, one ad hoc, the rest more or less familiar,
that we will use in examining this question.
Definition 28. If C ⊆ B are convex subsets of a real vector space V, with 0 ∈ B, we shall call C parkable
in B if there exists v ∈ C such that C − v ⊆ B. When clear from context, “in B” may be omitted.
If V is a real topological vector space, we will call sets of the form {x ∈ V | L(x) = a}, where L is a
nonzero continuous linear functional on V and a ∈ R, hyperplanes, while sets of the form {x ∈ V | L(x) ≥
a} will be called closed half-spaces.
A subset S of a vector space V will be called centrally symmetric if S = −S. A center of symmetry of
a subset S of V will mean a point v ∈ V such that S − v is centrally symmetric; equivalently, such that
S = 2v − S. (Note that a center of symmetry of a nonempty convex set belongs to that set.)
Lemma 29. Let B be a compact convex subset of Rn containing 0. Then the following conditions are
equivalent:
(44) The intersection of B with every hyperplane A that meets B is parkable.
(45) The intersection of B with every closed half-space H that meets B is parkable.
(46) Every nonempty closed convex subset C of B is parkable (= (43) above).
Proof. (46)⇒ (44) is clear; we will show (44)⇒ (45)⇒ (46).
(44)⇒ (45): Let H be a closed half-space in Rn, bounded by a hyperplane A, and meeting B. If H∩B
contains 0 it is trivially parkable, so assume the contrary. Thus B meets both H and its complement,
hence it meets their common boundary A, so by (44) there exists v ∈ A ∩ B such that (A ∩B) − v ⊆ B.
I claim that (H ∩ B) − v is also contained in B. Indeed, let p ∈ H ∩ B; we wish to show p − v ∈ B.
Intersecting our sets with the subspace of V spanned by p and v, and taking appropriate coordinates, we
may assume that n = 2, that A is the line {(x, y) | y = 1} ⊆ R2, and that v is the point (0, 1). H will
be the closed half-plane {(x, y) | y ≥ 1}, so we can write p = (xp, yp) with yp ≥ 1.
In this situation, A ∩ B will be a line segment (possibly degenerate) extending from a point (s, 1) to
a point (t, 1) (s ≤ t). Since (A ∩ B) − v ⊆ B, B also contains the segment from (s, 0) to (t, 0). Note
that if xp were > t, then the point where the line segment from p = (xp, yp) ∈ B to (t, 0) ∈ B meets A
would have x-coordinate > t, contradicting the assumption that A ∩ B terminates on the right at (t, 1);
so xp ≤ t. Similarly, xp ≥ s. Thus, xp ∈ [s, t], so (xp, 0) ∈ B. Hence p − v = (xp, yp−1) lies on the line
segment connecting p = (xp, yp) ∈ B with (xp, 0) ∈ B, hence lies in B, as claimed.
(45)⇒ (46): Suppose C is a nonempty closed convex subset of B which is not parkable. By compactness
of B, among the translates of C contained in B there is (at least) one that minimizes its distance to 0 in
the Euclidean norm on Rn; let us assume C itself has this property. Let p be the point of C nearest to
0 in that norm, and let A be the hyperplane passing through p and perpendicular (again in the Euclidean
norm) to p regarded as a vector. Then C will lie wholly in the half-space H bounded by A and not
containing 0. (For if we had q ∈ C not lying in H, then points close to p on the line segment from p to q
would be nearer to 0 than p is.) Assuming (45), H ∩B is parkable; say v ∈ H ∩B with (H ∩B)− v ⊆ B.
Since v ∈ H, if we write v as the sum ap+ q of a scalar multiple of p and a vector q perpendicular to p,
the coefficient a will be ≥ 1, and so in particular, positive. It follows that for sufficiently small positive c,
the point p − cv will be closer to 0 than p is; moreover, if we take such a c that is ≤ 1, (H ∩ B) − cv
will still be contained in B, since H ∩B and (H ∩B)− v are. Hence C − cv is contained in B, and has
a point p− cv which is closer to 0 than p is, contradicting our minimality assumption on C and p.
(In the above result, we could have replaced Rn by any real Hilbert space.)
Clearly, the closed Euclidean unit ball in Rn satisfies (44), and hence (45) and (46); hence since those
conditions are preserved by invertible linear transformations, so does the closed region enclosed by any
ellipsoid centered at 0. On the other hand, our example in the paragraph containing (23), of a normed
vector space in which (41) failed, had for its unit ball B a 3-cube centered at 0, showing that our properties
fail for that B. To see geometrically the failure of (44) for that B, choose a vertex of that cube and pass
a plane A through the three vertices adjacent thereto; it is not hard to see that A ∩ B is not parkable.
One can similarly show that none of the regular polyhedra centered at 0 satisfy (44), nor a circular cylinder
centered at 0, nor the solid obtained by attaching a hemisphere to the top and bottom of such a cylinder.
In fact, for n > 2, I know of no compact convex subset of Rn with nonempty interior that does satisfy that
condition, other than the regions enclosed by ellipsoids centered at 0. The situation is different for n = 2,
as shown by point (d) of the next result.
Lemma 30. Suppose B is a centrally symmetric convex subset of Rn. Then
(a) Any nonempty convex subset C ⊆ B that has a center of symmetry is parkable in B.
Hence, assuming in the remaining points that B is also compact, we have
(b) If the intersection of B with every hyperplane A that meets B has a center of symmetry, then B
satisfies the equivalent conditions (44)-(46).
In particular,
(c) If B is the closed region enclosed by an ellipsoid in Rn, then B satisfies (44)-(46), and
(d) If n = 2, then without further restrictions, B satisfies (44)-(46).
Proof. Let C be as in (a), with center of symmetry z ∈ C. Then for every x ∈ C, 2z − x ∈ C ⊆ B, so by
central symmetry of B, we have x− 2z ∈ B. Averaging x and x− 2z, we get x− z ∈ B. Thus C− z ⊆ B,
so C is parkable.
It follows that any B as in (b) satisfies (44), hence by Lemma 29, all of (44)-(46).
In the situation of (c), the intersection of B with a hyperplane A, if nonempty, is either a point or the
region enclosed by an ellipsoid in A, hence has a center of symmetry, while in (d) the intersection of B
with every line in R2 that meets B is a point or a closed line segment, hence has a center of symmetry; so
in each case, (b) gives the asserted conclusion.
Question 31. Let n ≥ 3, and suppose B is a compact convex subset of Rn having nonempty interior and
containing 0. Of the implications (i)⇒ (ii)⇒ (iii), which we have noted hold among the conditions listed
below, is either or both reversible?
(i) B is an ellipsoid centered at 0.
(ii) B is centrally symmetric, and for every hyperplane A meeting B, A ∩B has a center of symmetry.
(iii) Every closed convex subset of B is parkable in B.
Branko Grünbaum has pointed out to me a similarity between this question and the result of W.Blaschke
[3, pp.157–159] that if E is a smooth compact convex surface in R3 with everywhere nonzero Gaussian
curvature, such that when E is illuminated by parallel rays from any direction, the boundary curve of the
bright side lies in a plane, then E is an ellipsoid. I believe that methods similar to Blaschke’s may indeed
show that both implications of Question 31 are reversible. To see why, suppose B is a compact convex
subset of R3 with nonempty interior containing 0, which satisfies (iii) above, and whose boundary E is (as
in Blaschke’s result) a smooth surface with everywhere nonzero Gaussian curvature. Let A be any plane
through 0, and A′ the plane gotten by shifting A a small distance. Now the vectors that can possibly park
A′ ∩ B are constrained by the directions of the tangent planes to E at the points of A′ ∩ E (which are
well-defined because E is assumed smooth), and if we take A′ sufficiently close to A, these tangent planes
become close to the corresponding tangent planes at the points of A ∩ E. Applying the above observations
to planes A′ on both sides of A, one can deduce that all the tangent planes to E along A∩E must contain
vectors in some common direction (I am grateful to Bjorn Poonen for this precise formulation of a rough
idea I showed him); in other words, that A ∩ E is the boundary of the bright side when E is illuminated
by parallel rays from that direction. By definition, A∩E lies in the plane A; so we have the situation that
Blaschke considered, except that we have started with planarity and concluded that the curve is a boundary
of illumination, rather than vice versa.
That last difference is probably not too hard to overcome. More serious is the smoothness assumption
on E, used in both the above discussion and Blaschke’s argument. Finally, can the result be pushed from
n = 3 to arbitrary n ≥ 3 ? I leave it to those more skilled than I in the subject to see whether these ideas
can indeed be turned into a proof that (iii)⇒(i) in Question 31.
A related argument which can be extracted from a step in Blaschke’s development shows that a compact
convex subset B of R2 containing 0 and satisfying (46), whose boundary is a smooth curve containing
no line segments, must be centrally symmetric. Again, one would hope to remove the conditions on the
boundary.
One can ask about a converse to another of our observations:
Question 32. Suppose C is a compact convex subset of Rn (n > 2) such that for every centrally symmetric
compact convex subset B of Rn containing a translate C′ of C, the set C′ is parkable in B. Must C
have a center of symmetry?
Here the behavior of a given C can change depending on whether the dimension of the ambient vector
space is 2 or – as in the above question – larger: a triangle C has the above property in R2 by Lemma 30(d),
but not in R3, as we saw in the example where B was a cube.
Returning to the “pathology” which motivated the considerations of this section, one important case is
where the radius of a subset X of a normed vector space V decreases when V is embedded in a larger
normed vector space W. The next lemma determines how far down the radius of a given X can go.
Lemma 33. Let V be a normed vector space, and X a bounded subset of V. Then
(47) infW⊇V radW (X) = radV {(x− y)/2 | x, y ∈ X},
where W ranges over all normed vector spaces containing V. This infimum is realized by a W in which V
has codimension 1.
Proof. First consider any normed vector space W containing V, and suppose X is contained in the closed
ball of radius r about w ∈W. That ball has w as a center of symmetry, so it also contains {2w−y | y ∈ X},
hence taking midpoints of segments connecting that set to points x ∈ X, it contains {w+ (x− y)/2 | x, y ∈
X}. Translating by −w, we see that the ball of radius r about 0 contains {(x− y)/2 | x, y ∈ X}, so r is
at least the right-hand side of (47). This gives the inequality “≥ ” in (47); it remains to construct a W for
which radW (X) equals that right-hand side.
Before doing this, note that (47) holds trivially if X is empty or a singleton; so assuming it is neither,
let us re-scale and assume without loss of generality that the right-hand side of (47) equals 1. Since the set
whose radius is taken there is centrally symmetric, that set is contained in the closed unit ball BV of V.
(Cf. the proof of Lemma 30(a), which works not just for Rn, but for any normed vector space with B its
closed unit ball; or the proof of Lemma 5, applied to the 2-element group generated by x 7→ −x.) Now let
W = V ⊕ R, let us identify V with V × {0} ⊆ W, and let us take for the closed unit ball BW of W the
closure of the convex hull of
(48) {(x, 1) | x ∈ X} ∪ BV ∪ {(−x,−1) | x ∈ X}.
(We understand “closure” to mean “with respect to the product topology”, since we don’t have a norm until
we have made the above definition.) It is easy to see that any point in the convex hull of (48) whose second
coordinate is 0 is a convex linear combination of a point of BV and a member of the set on the right-hand
side of (47); but by assumption that set is contained in BV ; so in fact, BW ∩ V = BV , so the norm of W
indeed extends that of V.
But BW contains the translate {(x, 1) | x ∈ X} of X, hence X is contained in the closed ball of radius
1 about (0,−1), hence has radius ≤ 1 in W.
Even the case V = En is not immune to this phenomenon, since even in that case, the overspace W of
the above construction is generally not Euclidean. For instance, if we take for X an equilateral triangle in
2 centered at the origin, it is not hard to see that {(x− y)/2 | x, y ∈ X} is a hexagon whose vertices are
the midpoints of the edges a regular hexagon with the same circumcircle as X ; so the radius of X decreases
in W by the ratio of the inradius to the circumradius of a regular hexagon, in other words, by
This will not, of course, happen for a centrally symmetric X (cf. Lemma 30 or (47)). Other cases for
which it cannot happen depend on the metric structure: if X is a right or obtuse triangle in E2, or more
generally, any bounded set containing a diameter of a closed ball in which it lies, its radius clearly cannot
go down under extension of the ambient normed vector space (cf. Corollary 10).
8 Acknowledgements.
In addition to persons acknowledged above, I am indebted to W.Kahan for showing me an exercise he
had given his Putnam-preparation class, of proving Lemma 2 in E3, and for subsequently pointing out that
my solution to that exercise worked in any normed vector space; to Nik Weaver for information about his
results in [23], and to David Gale for pointing out the connection between the construction of §6 and results
in mathematical economics.
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George M. Bergman
Department of Mathematics
University of California
Berkeley, CA 94720-3840
gbergman@math.berkeley.edu
	The definition, and three examples.
	General properties of mapping radii.
	Some explicit mapping radii.
	Realizability of mapping radii.
	Related literature (and one more question).
	Appendix: The Arens-Eells space of X.
	Appendix: Translating convex sets to 0.
	Acknowledgements.
