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Convergence Properties of the NelderMead Simplex Method in Low Dimensions
 SIAM Journal of Optimization
, 1998
"... Abstract. The Nelder–Mead simplex algorithm, first published in 1965, is an enormously popular direct search method for multidimensional unconstrained minimization. Despite its widespread use, essentially no theoretical results have been proved explicitly for the Nelder–Mead algorithm. This paper pr ..."
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Cited by 599 (3 self)
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presents convergence properties of the Nelder–Mead algorithm applied to strictly convex functions in dimensions 1 and 2. We prove convergence to a minimizer for dimension 1, and various limited convergence results for dimension 2. A counterexample of McKinnon gives a family of strictly convex functions
An analysis of BGP convergence properties
 In SIGCOMM
"... The Border Gateway Protocol (BGP) is the de facto interdomain routing protocol used to exchange reachability information between Autonomous Systems in the global Internet. BGP is a pathvector protocol that allows each Autonomous System to override distancebased metrics with policybased metrics wh ..."
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Cited by 236 (14 self)
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when choosing best routes. Varadhan et al. [18] have shown that it is possible for a group of Autonomous Systems to independently define BGP policies that together lead to BGP protocol oscillations that never converge on a stable routing. One approach to addressing this problem is based on static
On the Convergence Properties
 of the EM Algorithm,” The Annals of Statistics
, 1983
"... open access www.bioinformation.net Software ..."
Convergence Properties of the KMeans Algorithms
"... This paper studies the convergence properties of the well known KMeans clustering algorithm. The KMeans algorithm can be described either as a gradient descent algorithm or by slightly extending the mathematics of the EM algorithm to this hard threshold case. We show that the KMeans algorithm act ..."
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Cited by 111 (2 self)
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This paper studies the convergence properties of the well known KMeans clustering algorithm. The KMeans algorithm can be described either as a gradient descent algorithm or by slightly extending the mathematics of the EM algorithm to this hard threshold case. We show that the KMeans algorithm
Convergence properties of the SIMP method
, 2003
"... The SIMP (Solid Isotropic Material with Penalization) method is used in Topology Optimization to solve problems where the variables are f0; 1g. The theoretical convergence properties are not exhaustively studied. In this paper a convergence theorem with weaker assumptions than the ones recently ..."
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The SIMP (Solid Isotropic Material with Penalization) method is used in Topology Optimization to solve problems where the variables are f0; 1g. The theoretical convergence properties are not exhaustively studied. In this paper a convergence theorem with weaker assumptions than the ones recently
On the Convergence Properties of the Hopfield Model
 Proc. IEEE vol.78 no.10
"... The main contribution is showing that the known convergence properties of the Hopfield model can be reduced to a very simple case, for which we have an elementary proof. The convergence properties of the Hopfield model are dependent on the structure of the interconnections matrix W and the method by ..."
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Cited by 18 (0 self)
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The main contribution is showing that the known convergence properties of the Hopfield model can be reduced to a very simple case, for which we have an elementary proof. The convergence properties of the Hopfield model are dependent on the structure of the interconnections matrix W and the method
CONVERGENCE PROPERTIES OF POLICY ITERATION
, 2004
"... This paper analyzes asymptotic convergence properties of policy iteration in a class of stationary, infinitehorizon Markovian decision problems that arise in optimal growth theory. These problems have continuous state and control variables and must therefore be discretized in order to compute an a ..."
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Cited by 17 (1 self)
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This paper analyzes asymptotic convergence properties of policy iteration in a class of stationary, infinitehorizon Markovian decision problems that arise in optimal growth theory. These problems have continuous state and control variables and must therefore be discretized in order to compute
On the convergence properties of contrastive divergence
 In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS
, 2010
"... Contrastive Divergence (CD) is a popular method for estimating the parameters of Markov Random Fields (MRFs) by rapidly approximating an intractable term in the gradient of the log probability. Despite CD’s empirical success, little is known about its theoretical convergence properties. In this pape ..."
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Cited by 8 (2 self)
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Contrastive Divergence (CD) is a popular method for estimating the parameters of Markov Random Fields (MRFs) by rapidly approximating an intractable term in the gradient of the log probability. Despite CD’s empirical success, little is known about its theoretical convergence properties
On Convergence Properties of the EM Algorithm for Gaussian Mixtures
 Neural Computation
, 1995
"... We build up the mathematical connection between the "ExpectationMaximization" (EM) algorithm and gradientbased approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P,and ..."
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Cited by 195 (15 self)
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,andwe provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages
Results 1  10
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