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» On the Complexity of Exclusion Algorithms for Optimization
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158
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WSC
2001
15 years 7 months ago
Global random optimization by simultaneous perturbation stochastic approximation
We examine the theoretical and numerical global convergence properties of a certain "gradient free" stochastic approximation algorithm called the "simultaneous pertu...
John L. Maryak, Daniel C. Chin
CORR
2011
Springer
183views Education» more  CORR 2011»
15 years 1 months ago
Mean-Variance Optimization in Markov Decision Processes
We consider finite horizon Markov decision processes under performance measures that involve both the mean and the variance of the cumulative reward. We show that either randomiz...
Shie Mannor, John N. Tsitsiklis
135
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CEJCS
2011
78views more  CEJCS 2011»
14 years 6 months ago
Good versus optimal: Why network analytic methods need more systematic evaluation
: Network analytic method designed for the analysis of static networks promise to identify significant relational patterns that correlate with important structures in the complex ...
Katharina Anna Zweig
APPML
2007
92views more  APPML 2007»
15 years 6 months ago
Topological grammars for data approximation
A method of topological grammars is proposed for multidimensional data approximation. For data with complex topology we define a principal cubic complex of low dimension and give...
Alexander N. Gorban, Neil R. Sumner, Andrei Yu. Zi...
ALDT
2009
Springer
142views Algorithms» more  ALDT 2009»
16 years 20 days ago
Finding Best k Policies
Abstract. An optimal probabilistic-planning algorithm solves a problem, usually modeled by a Markov decision process, by finding its optimal policy. In this paper, we study the k ...
Peng Dai, Judy Goldsmith