AI & ML
A First Closed-Form Robin Parameter for Optimized Schwarz Waveform Relaxation as a Function of Iteration Count, Overlap, and Time Window Length
arXiv cs.NA0 views
arXiv:2611.05576v1 Announce Type: new Abstract: Schwarz waveform relaxation (SWR) is a parallel-in-time iterative method for the solution of evolution problems and belongs to the class of Parallel-in-Time algorithms that have received increasing attention over the past three decades. For parabolic problems, SWR converges linearly over long time intervals and exhibits superlinear convergence on short time intervals. Since the introduction of optimized variants (OSWR) nearly three decades ago, determining the optimized Robin parameter when OSWR is applied on bounded time windows has remained an open problem. We introduce here a new analytical approach that, for the first time, yields a closed-form expression for the optimal Robin parameter in the transmission conditions for the one-dimensional heat equation with two overlapping subdomains. The resulting formula explicitly characterizes the dependence of the optimal parameter on the iteration number, the overlap size, and the length of the time window. Beyond resolving this long-standing optimization problem, our analysis provides a framework that can be extended to more general settings, including higher-dimensional problems, multiple subdomains, and higher-order transmission conditions.
Source:https://arxiv.org/abs/2611.05576PreviousUniform Race: Parameter-Free Approximate Rejection SamplingNextComparative analysis of numerical methods for X-ray 3D reconstruction from sparse views
Related posts
A Bayesian Optimized Biquad Filter-Burg Model for Real-Time EEG Phase Estimation
0arXiv eess.SP
Thought-deciphering via hierarchical EEG-to-text alignment
0arXiv eess.SP
Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells
0arXiv eess.SP
Benchmarking Sensor-Fault Robustness in Forecasting
0arXiv eess.SP
Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
0arXiv eess.SP
Multi-Threshold Sampling: Signal Space, Sampling Operators, and First Recorded Crossing-Time Distributions
0arXiv eess.SP