Results 291 to 300 of about 13,863,967 (339)
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On the Rate of Convergence of the Core
International Economic Review, 1979The classical theorem of Debreu and Scarf [1963] asserts that the core of a replicated pure exchange economy tends to the set of its competitive allocations as the index k of the replication (the number of traders of each type) tends to infinity.
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Journal of Scientific Computing, 2019
We discretize the stochastic Allen–Cahn equation with additive noise by means of a spectral Galerkin method in space and a tamed version of the exponential Euler method in time.
Meng Cai, S. Gan, Xiaojie Wang
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We discretize the stochastic Allen–Cahn equation with additive noise by means of a spectral Galerkin method in space and a tamed version of the exponential Euler method in time.
Meng Cai, S. Gan, Xiaojie Wang
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The Best Rate of Convergence of the Core
International Economic Review, 1983It has been demonstrated by \textit{R. J. Aumann} [Econometrica 43, 611-646 (1975; Zbl 0325.90082)] that the rate of convergence of the core can be arbitrarily slow unless some conditions are imposed on the limit economy. In the present paper an open set of limit economies is exhibited so that for each economy in the open set, a sequence of economies ...
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1990
In this chapter, the rate of convergence of the algorithm to its ODE and/or to the desired value θ* is described in more detail. The analysis is again asymptotic.
Albert Benveniste +2 more
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In this chapter, the rate of convergence of the algorithm to its ODE and/or to the desired value θ* is described in more detail. The analysis is again asymptotic.
Albert Benveniste +2 more
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The convergence rate of the MDM algorithm
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012In this paper we will describe a simple proof of a linear convergence rate for the MDM algorithm that solves the Minimum Norm Problem (MNP). Linear convergence rates have been shown for the SMO algorithm, but the proofs require specific assumptions and are rather involved. We will follow a different approach, with a more geometric flavor.
Jorge López Lázaro +1 more
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1997
The traditional definition of rate of convergence refers to the asymptotic properties of normalized errors about the limit point \( \bar \theta \). If e n = e for the Robbins—Monro algorithm, it is concerned with the asymptotic properties of \( U_n^ \in = \left( {\theta _n^ \in - \bar \theta } \right)/\sqrt \in \) for large n and small ∈.
Harold J. Kushner, G. George Yin
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The traditional definition of rate of convergence refers to the asymptotic properties of normalized errors about the limit point \( \bar \theta \). If e n = e for the Robbins—Monro algorithm, it is concerned with the asymptotic properties of \( U_n^ \in = \left( {\theta _n^ \in - \bar \theta } \right)/\sqrt \in \) for large n and small ∈.
Harold J. Kushner, G. George Yin
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Convergence rate on periodic gossiping
Information Sciences, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
F. He, S. Mou, J. Liu, A.S. Morse
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2015
In this chapter, we study the local rate of convergence of r n (x) to r(x). We obtain full information on the first asymptotic term of r n (x) − r(x), and are rewarded with (i) a central limit theorem for r n (x) − r(x), and (ii) a way of helping the user decide how to choose the weights v ni of the estimate.
Gérard Biau, Luc Devroye
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In this chapter, we study the local rate of convergence of r n (x) to r(x). We obtain full information on the first asymptotic term of r n (x) − r(x), and are rewarded with (i) a central limit theorem for r n (x) − r(x), and (ii) a way of helping the user decide how to choose the weights v ni of the estimate.
Gérard Biau, Luc Devroye
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1996
This chapter gives some results on rates of convergence of M-estimators, including maximum likelihood estimators and least-squares estimators. We first state an abstract result, which is a generalization of the theorem on rates of convergence in Chapter 3.2, and next discuss some methods to establish the maximal inequalities needed for the application ...
Aad W. van der Vaart, Jon A. Wellner
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This chapter gives some results on rates of convergence of M-estimators, including maximum likelihood estimators and least-squares estimators. We first state an abstract result, which is a generalization of the theorem on rates of convergence in Chapter 3.2, and next discuss some methods to establish the maximal inequalities needed for the application ...
Aad W. van der Vaart, Jon A. Wellner
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