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[This post is authored by Gil Kalai, who has kindly “guest blogged” this week’s “open problem of the week”. - T.]
This is a problem in discrete and convex geometry. It seeks to quantify the intuitively obvious fact that large convex bodies are so “fat” that they cannot avoid “detection” by a small number of observation points. More precisely, we fix a dimension d and make the following definition (introduced by Haussler and Welzl):
- Definition: Let be a finite set of points, and let . We say that a finite set is a weak -net for X (with respect to convex bodies) if, whenever B is a convex body which is large in the sense that , then B contains at least one point of Y. (If Y is contained in X, we say that Y is a strong -net for X with respect to convex bodies.)
For example, in one dimension, if , and where k is the integer part of , then Y is a weak -net for X with respect to convex bodies. Thus we see that even when the original set X is very large, one can create a -net of size as small as . Strong -nets are of importance in computational learning theory, and are fairly well understood via Vapnik-Chervonenkis (or VC) theory; however, the theory of weak -nets is still not completely satisfactory.
One can ask what happens in higher dimensions, for instance when X is a discrete cube . It is not too hard to cook up -nets of size (by using tools such as Minkowski’s theorem), but in fact one can create -nets of size as small as simply by taking a random subset of X of this cardinality and observing that “up to errors of “, the total number of essentially different ways a convex body can meet X grows at most polynomially in . (This is a very typical application of the probabilistic method.) On the other hand, since X can contain roughly disjoint convex bodies, each of which contains at least of the points in X, we see that no -net can have size much smaller than .
Now consider the situation in which X is now an arbitrary finite set, rather than a discrete cube. More precisely, let be the least number such that every finite set X possesses at least one weak -net for X with respect to convex bodies of cardinality at most . (One can also replace the finite set X with an arbitrary probability measure; the two formulations are equivalent.) Informally, f is the least number of “guards” one needs to place to prevent a convex body from covering more than of any given territory.
- Problem 1: For fixed d, what is the correct rate of growth of f as ?