72 search results - page 7 / 15 » Black-box optimization with a politician |
ICML
2003
IEEE
15 years 10 months ago
2003
IEEE
We present metric?? , a provably near-optimal algorithm for reinforcement learning in Markov decision processes in which there is a natural metric on the state space that allows t...
100
Voted
FOCS
2004
IEEE
15 years 1 months ago
2004
IEEE
Stochastic optimization problems attempt to model uncertainty in the data by assuming that (part of) the input is specified in terms of a probability distribution. We consider the...
CCO
2001
Springer
15 years 2 months ago
2001
Springer
Branch, cut, and price (BCP) is an LP-based branch and bound technique for solving large-scale discrete optimization problems (DOPs). In BCP, both cuts and variables can be generat...
78
Voted
DAGSTUHL
2007
14 years 11 months ago
2007
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
GLOBECOM
2009
IEEE
15 years 1 months ago
2009
IEEE
In this paper we discuss the design of optimization algorithms for cognitive wireless networks (CWNs). Maximizing the perceived network performance towards applications by selectin...