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Large-time behavior of perturbed diffusion Markov processes with applications to the second eigenvalue problem for Fokker-Planck operators and simulated annealing. (English) Zbl 0708.60056
Summary: We study the large-time behavior and rate of convergence to the invariant measures of the processes $dX^{\epsilon}(t)=b(X^{\epsilon}(t))dt+\epsilon \sigma (X^{\epsilon}(t))dB(t).$ A crucial constant $$\Lambda$$ appears naturally in our study. Heuristically, when the time is of the order $$\exp (\Lambda -\alpha)/\epsilon^ 2$$, the transition density has a good lower bound and when the process has run for about $$\exp (\Lambda +\alpha)/\epsilon^ 2$$, it is very close to the invariant measure. Let $$L^{\epsilon}=(\epsilon^ 2/2)\Delta -\nabla U\cdot \nabla$$ be a second-order differential operator on $${\mathbb{R}}^ d$$. Under suitable conditions, $$L^{\epsilon}$$ has the discrete spectrum $0=\lambda^{\epsilon}_ 1>-\lambda^{\epsilon}_ 2...\text{ and } \lim_{\epsilon \to 0}\epsilon^ 2 \log \lambda^{\epsilon}_ 2=- \Lambda.$ Let U be a function from $${\mathbb{R}}^ d$$ to $$[0,\infty)$$ with suitable conditions. A nonhomogeneous Markov process Y($$\cdot)$$ governed by $dY(t)=-\nabla U(Y(t))dt+\sqrt{c/\log (2+t)}dB(t),$ with $$Y(0)=x$$ is used to search for a global minimum of U. Let \b{S}$$=\{x |$$ $$U(x)=\min_{y}U(y)\}$$. There exists a critical constant $$d^*$$ such that for $$c>d^*$$, Y(t) uniformly converges to \b{S} in probability over x in a compact set.
The above statement fails for $$c<d^*$$. For $$c>\Lambda$$, Y(t) converges weakly to a probability measure which does not depend on the starting point x and concentrates on \b{S}. The techniques can also be used to study discrete simulated annealing.

##### MSC:
 60H10 Stochastic ordinary differential equations (aspects of stochastic analysis) 60J70 Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) 60F10 Large deviations 35P15 Estimates of eigenvalues in context of PDEs
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