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for fast convergence

by Ip Roy, Yan Wan, Ali Saberi, Mengran Xue
"... Designing linear distributed algorithms with memory ..."
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Designing linear distributed algorithms with memory

Fast Convergence of Selfish Rerouting

by Eyal Even-dar, Yishay Mansour - Proc. of the 16th ACM-SIAM Symposium on Discrete Algorithms (SODA), Vancouver, British Columbia (2005
"... We consider n anonymous selfish users that route their communication through m parallel links. The users are allowed to reroute, concurrently, from overloaded links to underloaded links. The different rerouting decisions are concurrent, randomized and independent. The rerouting process terminates wh ..."
Abstract - Cited by 25 (2 self) - Add to MetaCart
when the system reaches a Nash equilibrium, in which no user can improve its state. We study the convergence rate of several migration policies. The first is a very natural policy, which balances the expected load on the links, for the case that all users are identical and apply it, we show

Designing fast converging phylogenetic methods

by Luay Nakhleh , Usman Roshan, Katherine St. John , Jerry Sun , Tandy Warnow - IN PROC. 9TH INT’L CONF. ON INTELLIGENT SYSTEMS FOR MOLECULAR BIOLOGY (ISMB’01), VOLUME 17 OF BIOINFORMATICS , 2001
"... Absolute fast converging phylogenetic reconstruction methods are provably guaranteed to recover the true tree with high probability from sequences that grow only polynomially in the number of leaves, once the edge lengths are bounded arbitrarily from above and below. Only a few methods have been de ..."
Abstract - Cited by 6 (1 self) - Add to MetaCart
Absolute fast converging phylogenetic reconstruction methods are provably guaranteed to recover the true tree with high probability from sequences that grow only polynomially in the number of leaves, once the edge lengths are bounded arbitrarily from above and below. Only a few methods have been

The Fast Convergence of Incremental PCA

by Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
"... We consider a situation in which we see samples Xn ∈ Rd drawn i.i.d. from some distribution with mean zero and unknown covariance A. We wish to compute the top eigenvector of A in an incremental fashion: that is, with an algorithm that maintains an estimate of the top eigenvector, in O(d) space, and ..."
Abstract - Cited by 12 (0 self) - Add to MetaCart
, and incrementally adjusts the estimate with each new data point that arrives. Two classical such schemes are due to Krasulina (1969) and Oja (1983). We give finite-sample con-vergence rates for both. 1

The Fast Convergence of Boosting

by Matus Telgarsky
"... This manuscript considers the convergence rate of boosting under a large class of losses, including the exponential and logistic losses, where the best previous rate of convergence was O(exp(1/ɛ 2)). First, it is established that the setting of weak learnability aids the entire class, granting a rat ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
This manuscript considers the convergence rate of boosting under a large class of losses, including the exponential and logistic losses, where the best previous rate of convergence was O(exp(1/ɛ 2)). First, it is established that the setting of weak learnability aids the entire class, granting a

Fast convergence to Wardrop equilibria . . .

by Simon Fischer, et al.
"... ..."
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Fast convergence to an invariant measure for . . .

by Gregory F. Lawler, Brigitta Vermesi , 2010
"... ..."
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Abstract not found

Fast Convergence for Consensus in Dynamic Networks

by T-h. Hubert Chan, Li Ning
"... Abstract We study the convergence time required to achieve consensus in dynamic networks. In each time step, a node’s value is updated to some weighted average of its neighbors ’ and its old values. We study the case when the underlying network is dynamic, and investigate different averaging models. ..."
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. Both our analysis and experiments show that dynamic networks exhibit fast convergence behavior, even under very mild connectivity assumptions. 1

Fast Convergent Differential Evolution Algorithm

by Amrata Pupneja, Chakradhar Verma
"... Abstract — Differential Evolution (DE) algorithmis a well known population based stochastic algorithm used to solve optimization problems. But, DE, like other nature inspired algorithms, sometimes stuck in local optima and also suffers from the problem of stagnation. To resolve these issues and impr ..."
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the convergence speed of the search process. The proposed strategy is named as Fast Convergent Differential Evolution algorithm (FCDE). To prove efficiency and accuracy of FCDE, it is tested over 20 well known optimization problems. A comparative analysis has also been carried out among proposed FCDE, basic DE and

Fast convergence in evolutionary equilibrium selection

by Gabriel E. Kreindler, H. Peyton Young , 2011
"... Stochastic selection models provide sharp predictions about equilibrium selection when the noise level of the selection process is taken to zero. The difficulty is that, when the noise is extremely small, it can take an extremely long time for a large population to reach the stochastically stable ..."
Abstract - Cited by 11 (2 self) - Add to MetaCart
is sharp and convergence is fast for realistic noise levels and payoff values; moreover, the expected waiting times are comparable to those in local interaction models. 1. Stochastic stability and equilibrium selection Evolutionary models with random perturbations provide a useful framework for explaining
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