Mathematics > Optimization and Control
[Submitted on 3 Oct 2022 (v1), last revised 2 Nov 2022 (this version, v4)]
Title:Stochastic optimization of a mixed moving average process for controlling non-Markovian streamflow environments
View PDFAbstract:We investigated a cost-constrained static ergodic control problem of the variance of measure-valued affine processes and its application in streamflow management. The controlled system is a jump-driven mixed moving average process that generates realistic subexponential autocorrelation functions, and the static nature of the control originates from a realistic observability assumption in the system. The Markovian lift was effectively used to discretize the system into a finite-dimensional process, which is easier to analyze. The resolution of the problem is based on backward Kolmogorov equations and a quadratic solution ansatz. The control problem has a closed-form solution, and the variance has both strict upper and lower bounds, indicating that the variance cannot take an arbitrary value even when it is subject to a high control cost. The correspondence between the discretized system based on the Markovian lift and the original infinite-dimensional one is discussed. Then, a convergent Markovian lift is presented to approximate the infinite-dimensional system. Finally, the control problem was applied to real cases using available data for a river reach. An extended problem subject to an additional constraint on maintaining the flow variability was also analyzed without significantly degrading the tractability of the proposed framework.
Submission history
From: Hidekazu Yoshioka [view email][v1] Mon, 3 Oct 2022 07:47:28 UTC (15,165 KB)
[v2] Tue, 4 Oct 2022 05:50:12 UTC (15,164 KB)
[v3] Thu, 6 Oct 2022 08:38:31 UTC (15,163 KB)
[v4] Wed, 2 Nov 2022 11:30:20 UTC (15,001 KB)
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