Autoregressive processes in optimization

Autoregressive processes in optimization

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Article ID: iaor1988767
Country: Israel
Volume: 25
Issue: 2
Start Page Number: 302
End Page Number: 312
Publication Date: Jun 1988
Journal: Journal of Applied Probability
Authors:
Abstract:

Vector autoregresive 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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