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Central Limit Theorems for Compound Paths on the 2-Dimensional Lattice

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arxiv 1906.10645 v2 pith:6KW2ICKS submitted 2019-06-25 math.NT math.COmath.PR

classification math.NTmath.COmath.PR
keywords workchosendecompositiondecompositionslegalassumptioncombinatorialcomponent
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abstract

Zeckendorf proved that every integer can be written uniquely as a sum of non-consecutive Fibonacci numbers $\{F_n\}$, and later researchers showed that the distribution of the number of summands needed for such decompositions of integers in $[F_n, F_{n+1})$ converges to a Gaussian as $n\to\infty$. Decomposition problems have been studied extensively for a variety of different sequences and notions of a legal decompositions; for the Fibonacci numbers, a legal decomposition is one for which each summand is used at most once and no two consecutive summands may be chosen. Recently, Chen et al. [CCGJMSY] generalized earlier work to $d$-dimensional lattices of positive integers; there, a legal decomposition is a path such that every point chosen had each component strictly less than the component of the previous chosen point in the path. They were able to prove Gaussianity results despite the lack of uniqueness of the decompositions; however, their results should hold in the more general case where some components are identical. The strictly decreasing assumption was needed in that work to obtain simple, closed form combinatorial expressions, which could then be well approximated and led to the limiting behavior. In this work we remove that assumption through inclusion-exclusion arguments. These lead to more involved combinatorial sums; using generating functions and recurrence relations we obtain tractable forms in $2$ dimensions and prove Gaussianity again; a more involved analysis should work in higher dimensions.

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  1. Gaps of Summands of the Zeckendorf Lattice

    math.NT 2019-09 accept novelty 5.0 of 10

    For two-dimensional Zeckendorf lattice decompositions, the probability that a gap vector equals (v1, v2) converges to 1/2^(v1+v2), a bivariate geometric distribution.

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