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Current research in the mathematics of Gerrymandering involves the use of Markov Chain-based algorithms called the Flip and ReCom algorithms. The Flip algorithm creates new districts by checking for contiguity and adding vertices to a graph if contiguity is preserved, and removing vertices if equal population is violated. The ReCom algorithm creates new districts by creating spanning trees onto grid graphs, and bipartitioning these spanning trees if it preserves equal population. Our work focuses on using these methods to study the districting structures on m×n graphs and spanning trees on grid graphs.
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Zamora Flores, Jazmin, "Using Markov Chain Monte Carlo Algorithms to Predict Gerrymandering" (2021). Senior Projects Spring 2021. 59.
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