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We introduce a novel variance-reducing Monte Carlo algorithm for accuratedetermination of autocorrelation times. We apply this method to two-dimensionalIsing systems with sizes up to $15 \times 15$, using single-spin flip dynamics,random site selection and transition probabilities according to the heat-bathmethod. From a finite-size scaling analysis of these autocorrelation times, thedynamical critical exponent $z$ is determined as $z=2.1665$ (12)

Dynamic Exponent of the Two-Dimensional Ising Model and Monte Carlo Computation of the Subdominant Eigenvalue of the Stochastic Matrix