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We propose a minimal generalization of the celebrated Markov-Chain Monte Carlo algorithm which allows for an arbitrary number of configurations to be visited at every Monte Carlo step.
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This is often done by computing determinants or permanents of n × n n\times n matrices M ( S ) M(S) with edge weights as entries: ( M ( S ) ) i j = ℰ ( S ) [ i , j ] \left(M(S)\right)_{ij}=\mathcal{E}(S)[i,j]
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