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WSC
1997
15 years 1 months ago
Comparison of Monte Carlo and Deterministic Methods for Non-Adaptive Optimization
In this paper we compare the average performance of Monte Carlo methods for global optimization with non-adaptive deterministic alternatives. We analyze the behavior of the algori...
Hisham A. Al-Mharmah, James M. Calvin
AUTOMATICA
2007
82views more  AUTOMATICA 2007»
15 years 1 days ago
Simulation-based optimal sensor scheduling with application to observer trajectory planning
The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous....
Sumeetpal S. Singh, Nikolaos Kantas, Ba-Ngu Vo, Ar...
OL
2011
190views Neural Networks» more  OL 2011»
14 years 6 months ago
On optimality of a polynomial algorithm for random linear multidimensional assignment problem
We demonstrate that the Linear Multidimensional Assignment Problem with iid random costs is polynomially "-approximable almost surely (a. s.) via a simple greedy heuristic, f...
Pavlo A. Krokhmal
EOR
2008
69views more  EOR 2008»
14 years 12 months ago
On the complexity of optimization over the standard simplex
We review complexity results for minimizing polynomials over the standard simplex and unit hypercube. In addition, we derive new results on the computational complexity of approxi...
E. de Klerk, Dick den Hertog, G. Elabwabi
ML
2002
ACM
146views Machine Learning» more  ML 2002»
14 years 11 months ago
Variable Resolution Discretization in Optimal Control
Abstract. The problemof state abstractionis of centralimportancein optimalcontrol,reinforcement learning and Markov decision processes. This paper studies the case of variable reso...
Rémi Munos, Andrew W. Moore