In this paper, we propose a policy gradient reinforcement learning algorithm to address transition-independent Dec-POMDPs. This approach aims at implicitly exploiting the locality...
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...
Approximate symbolic computation problems can be formulated as constrained or unconstrained optimization problems, for example: GCD [3, 8, 12, 13, 23], factorization [5, 10], and ...
We study two problems, that of computing social optimum and that of finding fair allocations, in the congestion game model of Milchtaich[8] Although we show that the general prob...
We initiate a systematic study of the Row Deletion(B) problem on matrices: Given an input matrix A and a fixed "forbidden submatrix" B, the task is to remove a minimum n...
Sebastian Wernicke, Jochen Alber, Jens Gramm, Jion...