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Rate of Convergence of the Bundle Method
Journal of Optimization Theory and Applications, 2016The number of iterations needed by the bundle method for nonsmooth optimization to achieve a specified solution accuracy can be bounded by the product of the inverse of the accuracy and its logarithm, if the function is strongly convex.
Yu Du, A. Ruszczynski
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Rate of Convergence of Recursive Estimators
SIAM Journal on Control and Optimization, 1992Summary: It is proved that the sequence of recursive estimators generated by Ljung's scheme combined with a suitable restarting mechanism converges under certain conditions with rate \(O_ M(n^{-1/2})\), where the rate is measured by the \(L_ q\)-norm of the estimation error for any \(1\leq ...
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Rate of Convergence of Positive Series
Ukrainian Mathematical Journal, 2004Summary: We investigate the rate of convergence of series of the form \[ F(x)=\sum_{n=0}^{\infty}a_n\exp\{x\lambda_n+\tau(x)\beta_n\}, \quad a_n\geq0,\;n\geq 1,\;a_0=1, \] where \(\lambda=(\lambda_n)\), \(0 ...
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1996
In this chapter we consider the general pattern recognition problem: Given the observation X and the training data D n = ((X 1, Y 1),..., (X n , Y n )) of independent identically distributed random variable pairs, we estimate the label Y by the decision . The error probability is .
Luc Devroye +2 more
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In this chapter we consider the general pattern recognition problem: Given the observation X and the training data D n = ((X 1, Y 1),..., (X n , Y n )) of independent identically distributed random variable pairs, we estimate the label Y by the decision . The error probability is .
Luc Devroye +2 more
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Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations
Journal of machine learning research, 2017In this paper we propose a modified version of the simulated annealing algorithm for solving a stochastic global optimization problem. More precisely, we address the problem of finding a global minimizer of a function with noisy evaluations. We provide a
Clément Bouttier, Ioana Gavra
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Subgeometric Rates of Convergence
2018We have seen in Chapter 11 that a recurrent irreducible kernel P on \(\mathsf {X}\times \mathscr {X}\) admits a unique invariant measure that is a probability measure \(\pi \) if the kernel is positive. If the kernel is, moreover, aperiodic, then the iterates of the kernel \(P^n(x,\cdot )\) converge to \(\pi \) in f-norm for \(\pi \)-almost all \(x\in \
Randal Douc +3 more
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2009
In applications of asymptotic theorems of spectral analysis of large dimensional random matrices, one of the important problems is the convergence rate of the ESD. It had been puzzling probabilists for a long time until the papers of Bai [16, 17] were published.
Zhidong Bai, Jack W. Silverstein
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In applications of asymptotic theorems of spectral analysis of large dimensional random matrices, one of the important problems is the convergence rate of the ESD. It had been puzzling probabilists for a long time until the papers of Bai [16, 17] were published.
Zhidong Bai, Jack W. Silverstein
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Geometric Rates of Convergence
2018We have seen in Chapter 11 that a positive recurrent irreducible kernel P on \(\mathsf {X}\times \mathscr {X}\) admits a unique invariant probability measure, say \(\pi \). If the kernel is, moreover, aperiodic, then the iterates of the kernel \(P^n(x,\cdot )\) converge to \(\pi \) in total variation distance for \(\pi \)-almost all \(x\in \mathsf {X}\)
Randal Douc +3 more
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Rates of Convergence for Double Sequences
Southeast Asian Bulletin of Mathematics, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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