Generalizing two DPP-based Monte Carlo estimators to continuous domains provides variance rates of O(N^{-(1+1/d)}) for a fixed DPP method and O(1/N) for a tailored DPP method, along with new sampling algorithms.
MethodsofNumericalIntegration .ComputerScience and Applied Mathematics
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Domain-of-dependence stabilization for cut-cell meshes achieves fully discrete stability for linear advection under a CFL condition independent of arbitrarily small cell sizes.
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On two ways to use determinantal point processes for Monte Carlo integration
Generalizing two DPP-based Monte Carlo estimators to continuous domains provides variance rates of O(N^{-(1+1/d)}) for a fixed DPP method and O(1/N) for a tailored DPP method, along with new sampling algorithms.
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The domain-of-dependence stabilization for cut-cell meshes is fully discretely stable
Domain-of-dependence stabilization for cut-cell meshes achieves fully discrete stability for linear advection under a CFL condition independent of arbitrarily small cell sizes.