This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood a...
Abstract. We explore a new general-purpose heuristic for nding highquality solutions to hard optimization problems. The method, called extremal optimization, is inspired by self-or...
Stefan Boettcher, Allon G. Percus, Michelangelo Gr...
We study the problem of minimizing the expected cost of binary searching for data where the access cost is not fixed and depends on the last accessed element, such as data stored i...
Gonzalo Navarro, Ricardo A. Baeza-Yates, Eduardo F...
We consider the setting of a device that obtains it energy from a battery and some regenerative source such as a solar cell. We consider the speed scaling problem of scheduling a c...
We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean spaces, in the setting where k is part of the input (not a constant). For the k-means pr...