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Generating uniform random vectors in \(\mathbb Z^k_p\): the general case. (English) Zbl 1228.60078
Summary: We consider the rate of convergence of the Markov chain \({\mathbf X}_{n+1}=A{\mathbf X}_n+\mathbf B_n (\operatorname{mod} p)\), where \(A\) is an integer matrix with nonzero eigenvalues, and \(\{{\mathbf B}_n\}_n\) is a sequence of independent and identically distributed integer vectors, with support not parallel to a proper subspace of \({\mathbf Q}^k\) invariant under \(A\). If \(|\lambda_i|\neq1\) for all eigenvalues \(\lambda_i\) of \(A\), then \(n=O((\ln p)^2\)) steps are sufficient and \(n=O(\ln p)\) steps are necessary to have \({\mathbf X}_n\) sampling from a nearly uniform distribution. Conversely, if \(A\) has the eigenvalues \(\lambda_i\) that are roots of positive integer numbers, \(|\lambda_i|=1\) and \(|\lambda_i|>1\) for all \(i\neq1\), then \(O(p^2)\) steps are necessary and sufficient.

60J10 Markov chains (discrete-time Markov processes on discrete state spaces)
60F15 Strong limit theorems
Full Text: DOI arXiv
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