In this paper the distributed Constraint Satisfaction Ant Algorithm (CSAA) framework is presented. It uses an ant-based system for the distributed solving of constraint satisfacti...
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...
We consider discrete stochastic optimization problems where the objective function can only be estimated by a simulation oracle; the oracle is defined only at the discrete points....
We describe a simple iterative method for proving a variety of results in combinatorial optimization. It is inspired by Jain’s iterative rounding method (FOCS 1998) for designing...
Motivated by Ajtai’s worst-case to average-case reduction for lattice problems, we study the complexity of computing short linearly independent vectors (short basis) in a lattic...