Abstract In this paper we investigate the composition of cheap network storage resources to meet specific availability and capacity requirements. We show that the problem of find...
: Recurrent neural networks possess interesting universal approximation capabilities, making them good candidates for time series modeling. Unfortunately, long term dependencies ar...
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a beli...
We propose an SQP-type algorithm for solving nonlinear second-order cone programming (NSOCP) problems. At every iteration, the algorithm solves a convex SOCP subproblem in which th...
We consider approximation algorithms for buy-at-bulk network design, with the additional constraint that demand pairs be protected against edge or node failures in the network. In...
Spyridon Antonakopoulos, Chandra Chekuri, F. Bruce...