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Autoregressive processes in optimization

Published online by Cambridge University Press:  14 July 2016

Tomáš Cipra
Tomáš Cipra*
Affiliation:
Charles University of Prague
*
∗ Postal address: Department of Statistics, Charles University, Sokolovská 83, 186 00 Prague 8, Czechoslovakia.
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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.

Keywords

GEOMETRIC ERGODICITYSTOCHASTIC PROGRAMMING

Information

Type
Research Papers
Information
Journal of Applied Probability , Volume 25 , Issue 2 , June 1988 , pp. 302 - 312
Copyright
Copyright © Applied Probability Trust 1988 

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