It states that the Minkowski sum of a large number of sets is approximately convex. The clearest statement as well as the nicest proof I am familiar with is due to J. W. S. Cassels. Cassels is a distinguished number theorist who for many years taught the mathematical economics course in the Tripos. The lecture notes are available in a slender book now published by Cambridge University Press.
This central limit like quality of the lemma is well beyond the capacity of a hewer of wood like myself. I prefer the more prosaic version.
Let be a collection of sets in with . Denote by the Minkowski sum of the collection . Then, every can be expressed as where for all and .
How might this be useful? Let be an 0-1 matrix and with . Consider the problem
Let be a solution to the linear relaxation of this problem. Then, the lemma yields the existence of a 0-1 vector such that and . One can get a bound in terms of Euclidean distance as well.
How does one do this? Denote each column of the matrix by and let . Let . Because and it follows that . Thus, by the Lemma,
where each and . In words, has at most fractional components. Now construct a 0-1 vector from as follows. If , set . If is fractional, round upto 1 with probability and down to zero otherwise. Observe that and the . Hence, there must exist a 0-1 vector with the claimed properties.
The error bound of is to large for many applications. This is a consequence of the generality of the lemma. It makes no use of any structure encoded in the matrix. For example, suppose were an extreme point and a totally unimodular matrix. Then, the number of fractional components of $x^*$ are zero. The rounding methods of Kiralyi, Lau and Singh as well as of Kumar, Marathe, Parthasarthy and Srinivasan exploit the structure of the matrix. In fact both use an idea that one can find in Cassel’s paper. I’ll follow the treatment in Kumar et. al.
As before we start with . For convenience suppose for all . As as has more columns then rows, there must be a non-zero vector in the kernel of , i.e., . Consider and . For and sufficiently small, for all . Increase and until the first time at least one component of and is in . Next select the vector with probability or the vector with probability . Call the vector selected .
Now . Furthermore, has at least one more integer component than . Let . Let be the matrix consisting only of the columns in and consist only of the components of in . Consider the system . As long as has more columns then rows we can repeat the same argument as above. This iterative procedure gives us the same rounding result as the Lemma. However, one can do better, because it may be that even when the number of columns of the matrix is less than the number of rows, the system may be under-determined and therefore the null space is non-empty.
In a sequel, I’ll describe an optimization version of the Lemma that was implicit in Starr’s 1969 Econometrica paper on equilibria in economies with non-convexities.