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Last-passage percolation and product-matrix ensembles

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arxiv 2503.22801 v1 pith:2WP4XGTU submitted 2025-03-28 math.PR math-phmath.MP

classification math.PRmath-phmath.MP
keywords last-passageprocesstimeblockblocksrandomarrayaverage
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We introduce and study a model of directed last-passage percolation in planar layered environment. This environment is represented by an array of random exponential clocks arranged in blocks, for each block the average waiting times depend only on the local coordinates within the block. The last-passage time, the total time needed to travel from the source to the sink located in a given block, maximized over all the admissible paths, becomes a stochastic process indexed by the number of blocks in the array. We show that this model is integrable, particularly the probability law of the last-passage time process can be determined via a Fredholm determinant of the kernel that also appears in the study of products of random matrices. Further, we identify the scaling limit of the last-passage time process, as the sizes of the blocks become infinitely large and the average waiting times become infinitely small. Finite-dimensional convergence to the continuous-time critical stochastic process of random matrix theory is established.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Local Statistics of Singular Values for Products of Truncated Unitary Matrices

    math.PR 2025-06 conditional novelty 6.0 of 10

    Products of truncated unitary matrices exhibit a universal three-phase transition in local singular value statistics, interpolating between Gaussian and GUE universality as the modified depth-to-width ratio varies fro...

  2. Edge statistics for singular values of products of rectangular complex Ginibre matrices

    math.PR 2025-07 conditional novelty 5.0 of 10

    For products of rectangular complex Ginibre matrices, the depth-to-width ratio Delta is the sharp threshold: Delta tending to 0 gives Airy kernel edge statistics, Delta tending to infinity gives Gaussian edge fluctuations.

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