A probabilistic analysis of a measure of combinatorial complexity for the central curve

Abstract.

We investigate certain combinatorial properties of the central curve associated with interior point methods for linear optimization. We define a measure of complexity for the curve in terms of the number of turns, or changes of direction, that it makes in a geometric sense, and then perform an average case analysis of this measure for P-matrix linear complementarity problems. We show that the expected number of nondegenerate turns taken by the central curve is bounded by n ²-n, where the expectation is taken with respect to a sign-invariant probability distribution on the problem data. As an alternative measure of complexity, we also consider the number of times the central curve intersects with a wide class of algebraic hypersurfaces, including such objects as spheres and boxes. As an example of the results obtained, we show that the primal and dual variables in each coordinate of the central curve cross each other at most once, on average. As a further example, we show that the central curve intersects any sphere centered at the origin at most twice, on average.