The contraction method for recursive algorithms

Algorithmica ◽  
2001 ◽  
Vol 29 (1-2) ◽  
pp. 3-33 ◽  
Author(s):  
U. Rösler ◽  
L. Rüschendorf
Keyword(s):  
Recursive Algorithms ◽  
Contraction Method

Probability metrics and recursive algorithms

1995 ◽  
Vol 27 (3) ◽  
pp. 770-799 ◽  
Author(s):  
S. T. Rachev ◽  
L. Rüschendorf
Keyword(s):  
Asymptotic Behaviour ◽  
Recursive Algorithms ◽  
Probability Metrics ◽  
Contraction Method ◽  
Probability Metric ◽  
Wide Range

It is shown by means of several examples that probability metrics are a useful tool to study the asymptotic behaviour of (stochastic) recursive algorithms. The basic idea of this approach is to find a ‘suitable' probability metric which yields contraction properties of the transformations describing the limits of the algorithm. In order to demonstrate the wide range of applicability of this contraction method we investigate examples from various fields, some of which have already been analysed in the literature.

Probability metrics and recursive algorithms

1995 ◽  
Vol 27 (03) ◽  
pp. 770-799 ◽  
Author(s):  
S. T. Rachev ◽  
L. Rüschendorf
Keyword(s):  
Asymptotic Behaviour ◽  
Recursive Algorithms ◽  
Probability Metrics ◽  
Contraction Method ◽  
Probability Metric ◽  
Wide Range

It is shown by means of several examples that probability metrics are a useful tool to study the asymptotic behaviour of (stochastic) recursive algorithms. The basic idea of this approach is to find a ‘suitable' probability metric which yields contraction properties of the transformations describing the limits of the algorithm. In order to demonstrate the wide range of applicability of this contraction method we investigate examples from various fields, some of which have already been analysed in the literature.

scholarly journals Recursive algorithms for the computation of the potential harmonic coefficients of a constant density polyhedron

2009 ◽  
Vol 83 (10) ◽  
pp. 925-942 ◽  
Author(s):  
Dimitrios Tsoulis ◽  
Olivier Jamet ◽  
Jérôme Verdun ◽  
Nicolas Gonindard
Keyword(s):  
Constant Density ◽  
Recursive Algorithms ◽  
Potential Harmonic

scholarly journals APPLICATION OF AN ADAPTIVE STEP-SIZE ALGORITHM IN MODELS OF HYPERINFLATION

2012 ◽  
Vol 16 (S3) ◽  
pp. 355-375 ◽  
Author(s):  
Olena Kostyshyna
Keyword(s):  
New York ◽  
Mean Squared Error ◽  
American Economic Review ◽  
Sufficient Information ◽  
Recursive Algorithms ◽  
Time Varying ◽  
Step Size ◽  
Adaptive Step Size ◽  
Squared Error ◽  
Adaptive Step

An adaptive step-size algorithm [Kushner and Yin,Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed., New York: Springer-Verlag (2003)] is used to model time-varying learning, and its performance is illustrated in the environment of Marcet and Nicolini [American Economic Review93 (2003), 1476–1498]. The resulting model gives qualitatively similar results to those of Marcet and Nicolini, and performs quantitatively somewhat better, based on the criterion of mean squared error. The model generates increasing gain during hyperinflations and decreasing gain after hyperinflations end, which matches findings in the data. An agent using this model behaves cautiously when faced with sudden changes in policy, and is able to recognize a regime change after acquiring sufficient information.

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