Spectral decomposition of the logit Jacobian yields an adaptive MSA with linear convergence and a tractable Newton method for path-based SUE, with reported speedups on networks up to Chicago Regional size.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Discriminator-informed resampling via normalizing flows reduces error in the EnGMF for low-ensemble regimes on the Ikeda map and Lorenz '63 system.
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Spectral analysis of the logit mapping and implications for stochastic user equilibrium algorithms
Spectral decomposition of the logit Jacobian yields an adaptive MSA with linear convergence and a tractable Newton method for path-based SUE, with reported speedups on networks up to Chicago Regional size.
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Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach
Discriminator-informed resampling via normalizing flows reduces error in the EnGMF for low-ensemble regimes on the Ikeda map and Lorenz '63 system.