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Results 1 - 10
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5,322
Fast Convergence of Selfish Rerouting
- 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 ..."
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Cited by 25 (2 self)
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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
- 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 ..."
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Cited by 6 (1 self)
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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
"... 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 ..."
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Cited by 12 (0 self)
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, 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
"... 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 ..."
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Cited by 3 (0 self)
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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 for Consensus in Dynamic Networks
"... 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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Cited by 1 (0 self)
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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
"... 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
, 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 ..."
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Cited by 11 (2 self)
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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
Results 1 - 10
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5,322
