A quenched invariance principle for non-elliptic random walk in i.i.d. balanced random environment
classification
🧮 math.PR
keywords
randomenvironmentwalkbalancedellipticresultinvariancenearest
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We consider a random walk on Z^d in an i.i.d. balanced random environment, that is a random walk for which the probability to jump from x to nearest neighbor x+e is the same as to nearest neighbor x-e. Assuming that the environment is genuinely d-dimensional and balanced we show a quenched invariance principle: for P almost every environment, the diffusive rescaled random walk converges to a Brownian motion with deterministic non-degenerate diffusion matrix. Within the i.i.d. setting, our result extend both Lawler's uniformly elliptic result and Guo and Zeitouni's elliptic result to the general (non elliptic) case. Our proof is based on analytic methods and percolation arguments.
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