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JOURNALS // Teoriya Veroyatnostei i ee Primeneniya // Archive

Teor. Veroyatnost. i Primenen., 1972 Volume 17, Issue 2, Pages 281–295 (Mi tvp2529)

This article is cited in 30 papers

Some problems concerning stability under small stochastic perturbations

A. D. Venttsel', M. I. Freidlin

Moscow

Abstract: Let $x_0$ be a stable equilibrium point of a dynamic system $\dot x=b(x)$ in $R^r$; a Markov stochastic process $x_t^\varepsilon$ appears as a result of small stochastic perturbations of this system: $dx_t^\varepsilon=b(x_t^\varepsilon)\,dt+\varepsilon\sigma(x_t^\varepsilon)\,d\xi_t$ where $\xi_t$ is a Wiener process. Problems concerning stability of the point $x_0$ with respect to the stochastic process $x_t^\varepsilon$ are considered.
All trajectories of the process $x_t^\varepsilon$ sooner or later, leave each neighbourhood of the equilibrium point. The problem arises how to choose a region of a given area for which the mean exit time is maximum? Another problem setting: suppose that one can control the process $x_t^\varepsilon$ by chosing a drift vector $b(x)$ at each point $x$ of some set of vectors $\Pi(x)$. How should one control the process so that the mean exit time of a given region would be maximum (minimum)? Asymptotically optimal solutions to these questions are given: the control proposed by the authors is not worse (not essentially worse) than any other control if $\varepsilon$ is sufficiently small; the mean exit time of any other region $G$ of a given area is less than that of the region the authors point at if $\varepsilon$ is small.
The way of solving these problems is to estimate the main term of the mean exit time of a given region $G$ when $\varepsilon\to0$. This main term is $\exp\Bigl\{\frac1{2\varepsilon^2}\min\limits_{y\in\partial G}V(x_0,y)\Bigr\}$ where $V(x_0,x)$ is a function that does not depend on the region and can be found as a solution of a specific problem for the differential equation
$$ \sum a^{ij}(x)\frac{\partial V}{\partial x^i}\frac{\partial V}{\partial x^j}+4\sum b^i(x)\frac{\partial V}{\partial x^i}=0,\quad(a^{ij}(x))=\sigma(x)\sigma^*(x). $$
In order to solve the optimal control problem, a non-linear partial differential equation is considered. In the case of shift-invariance this equation can be solved by means of a certain geometrical construction.

Received: 23.07.1970


 English version:
Theory of Probability and its Applications, 1973, 17:2, 269–283

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