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Autoregressive processes in optimization
Published online by Cambridge University Press: 14 July 2016
Abstract
Vector autoregressive processes of the first order are considered which are non-negative and optimize a linear objective function. These processes may be used in stochastic linear programming with a dynamic structure. By using Tweedie's results from the theory of Markov chains, conditions for geometric rates of convergence to stationarity (i.e. so-called geometric ergodicity) and for existence and geometric convergence of moments of these processes are obtained.
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- Copyright © Applied Probability Trust 1988
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