This is another post in my ongoing series of missives from the algorithms seminar I ran last semester (yes I know, I'm late: I have a good excuse though, and it currently weighs 6 lb 11oz).

One of the most exciting developments in the area of high dimensional geometry was the introduction of the idea of core sets. The basic premise is this: suppose we have a geometric optimization problem on a set of points P of size n (Amen!) that can be solved exactly in time p(n) (where p might be some relatively unpleasant polynomial in n). Suppose we can select a set of points S (not necessarily contained in P) of size g(1/epsilon) (the "core-set") such that the value of the optimal solution on S is close (i.e within a 1+epsilon factor) to the value on P, then we can run our expensive algorithm on S, rather than on P. The new running time is n (typically) to compute S, plus p(g(1\epsilon)) to obtain the approximate solution. What we've done here is construct a linear-time approximation scheme for our optimization.

There's a bit of nice magic here: we are essentially claiming that a set of points with size independent of the input can suffice to give a good approximation to the desired answer.

There are many ways of understanding the real power of core sets, and when they exist: perhaps the most surprising result is that for certain high dimensional problems, it is possible to find a core-set that has size independent of both the input size and the input dimension, thus circumventing the famed curse of dimensionality. But that's not what I want to discuss here. Rather, I want to present the view of core sets as a kind of generalized grid: I find this viewpoint less "magical" and often useful when trying to figure out when a core-set might actually exist.

Sparsifying via a grid:

Suppose we have a large collection of points in the plane, and we wish to "shrink" the input to some manageable size. The first thing we can try is to lay down a grid, with side length R, and snap each point to the center of the grid cell it's in. If n is large compared to R, we'd expect many points to lie in each cell. If the diameter of the point set is D, then we need (D/R)^2 grid cells.

There are two problems with this: firstly, D could be much larger than n. In fact, since D is a function of the coordinates of the input, it could be exponentially larger than n ! Secondly, the perturbation of each point to the center of the grid incurs an error of possibly R/\sqrt{2}, and so R needs to be very smalll.

Consider the problem of computing the minimum enclosing ball of a collection of points. The radius of this ball is within a constant factor of D by the triangle inequality. This means that if we're willing to tolerate a multiplicative error in our bounds, we can perturb each point by epsilon*D, while maintaining an epsilon-approximation. Here, setting R = epsilon' D suffices, and we only need O(1/epsilon^2) cells. Voila ! a core-set.

Of course, we're not going to be this lucky all the time: for example, if we want to compute the width (the smallest distance between parallel lines that bound the point set), then the diameter has no relevance: the width of a point set can be zero even if its diameter is large (if all points are on a line). In that case, how would we choose a grid size that would work for us ?

Approximating all directions:

The answer, as it turns out, is to try and approximate the "diameter" in all directions. More precisely, we can define the extent of a point set along a certain direction as the diameter of the projection along that direction. Having done so, the property we desire of our "grid" is that it approximates this extent function along each direction. For many interesting optimization problems, the extents suffice to compute the optimal solution, and so approximating the extents suffices to approximate the solution.

The easiest way of approximating extents would be to compute the convex hull of the point set. This might not be a sparse representation in general. A better answer is to place a grid, and pick out all non-empty grid cells along the boundary (actually, even picking the extreme cells along each vertical direction suffices).

Notice that by concerning ourselves only with extents, we have essentially eliminated the problem of large diameters (because we can always scale the point set). What remains is the problem of variation in the extents. If a point set has a very small extent along a direction (say the direction that defines its width), then in order to capture that extent correctly, we'd need a very fine grid resolution.

Exploiting fatness:

The problem here appears to arise from the fact that a point set can be skinny. The notion of fatness in computational geometry is a very powerful one: there are many ways of defining it, but in all cases, the measure captures the degree to which a convex shape is "rounded" versus "skinny" (one definition is the ratio between the minimum enclosing ball and the largest inscribed ball). If a point set is alpha-fat, then upto a (constant) factor alpha, it's round, and therefore has comparable extents in all directions.

Performing an affine transform can make a point set alpha-fat (for any constant alpha). Although an affine transform will play havoc with distances, it preserves ratios: in particular, the ratio between exact and approximate extents.

And with this, we are done. We transform the point set so that it's alpha-fat, and choose a grid size that's a function of alpha and epsilon (after scaling the point set so that the diameter is 1). Then we snap all points to grid cells as before, and pick up the highest and lowest grid cells in each vertical column. What we get is a point set that captures the important characteristics of the input, but whose size depends solely on the error parameters: in other words, a core-set.

Extensions:

What makes this idea particularly powerful is that it can be extended. For example, we can construct core sets for the "extents" of a set of linear functions by going to the dual space. Similarly, we can construct core sets for collections of polynomials by using the linearization trick and then going to the dual space (in higher dimensions). But ultimately, it boils down to constructing a clever grid.

Note: most of this exposition comes from a reading of Geometric Approximation via Coresets, by Pankaj Agarwal, Sariel Har-Peled and Kasturi Varadarajan.