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Walking on Spheres and Talking to Neighbors: Variance Red...
[Submitted on 26 Apr 2024 (v1), last revised 2 Jul 2026 (this ve · 2024-04-27 · via math updates on arXiv.org

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Abstract:Walk on Spheres algorithms leverage properties of Brownian Motion to create Monte Carlo estimates of solutions to a class of elliptic partial differential equations. We propose a new caching strategy which leverages the continuity of paths of Brownian Motion. In the case of Laplace's equation with Dirichlet boundary conditions, our algorithm has improved asymptotic runtime compared to previous approaches. Until recently, estimates were constructed pointwise and did not use the relationship between solutions at nearby points within a domain. Instead, our results are achieved by passing information from a cache of fixed size. We also provide bounds on the performance of our algorithm and demonstrate its performance on example problems of increasing complexity.

Submission history

From: Michael Czekanski [view email]
[v1] Fri, 26 Apr 2024 20:44:36 UTC (1,494 KB)
[v2] Tue, 8 Apr 2025 19:26:41 UTC (1,582 KB)
[v3] Thu, 2 Jul 2026 02:47:19 UTC (10,220 KB)