In this paper we study the k-means clustering problem. It is well-known that the general version of this problem is NP-hard. Numerous approximation algorithms have been proposed fo...
We propose a general method for estimating the distance between a compact subspace K of the space L1 ([0, 1]s ) of Lebesgue measurable functions defined on the hypercube [0, 1]s ,...
The objective of this paper is to characterize classes of problems for which a greedy algorithm finds solutions provably close to optimum. To that end, we introduce the notion of k...
Approximations can aim at having close to optimal value or, alternatively, they can aim at structurally resembling an optimal solution. Whereas value-approximation has been extensi...
Approximation of the permanent of a matrix with nonnegative entries is a well studied problem. The most successful approach to date for general matrices uses Markov chains to appr...