Designing linear distributed algorithms with memory

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

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

, 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

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

. Both our analysis and experiments show that dynamic networks exhibit fast convergence behavior, even under very mild connectivity assumptions. 1

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

Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error rates higher than those of C4.5 and C4.5rules. We then propose a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5rules with respect to error rates, but much more efficient on large samples. RIPPERk obtains error rates lower than or equivalent to C4.5rules on 22 of 37 benchmark problems, scales nearly linearly with the number of training examples, and can efficiently process noisy datasets containing hundreds of thousands of examples.

Abstract. We say that the series of general term un = 0 is fast converging if log |un | ≤ c2n for some c < 0. We prove irrationality results and compute irrationality measures for some fast converging se-ries of rational numbers, by using Mahler’s transcendence method in the form introduced

interaction. Selection 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