On tide propagation in convergent estuaries

Convergent Propagation Algorithms via Oriented Trees

Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have previously been suggested as methods for solving these optimization problems. However, there are few convergence guarantees for such algorithms, and the algorithms are therefore not guaranteed to solve the corresponding ...

Effect of bed roughness on the propagation of negative surges in rivers and estuaries

In an open channel, a sudden drop in water elevation is associated with a negative surge. Practical applications include the ebb tide motion in estuaries with macro-tidal conditions, the rundown of the swash, and the retreat of tsunami waters after reaching the maximum runup. Herein a physical study is presented with a focus on the effects of bed roughness. Detailed measurements were performed ...

Anthropogenic Impacts on Estuaries

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from [15] with covariance decoupling techniques [23, 13], it runs at least an order of magnitude faster than the most common EP solver.

New globally convergent training scheme based on the resilient propagation algorithm

In this paper, a new globally convergent modification of the Resilient Propagation-Rprop algorithm is presented. This new addition to the Rprop family of methods builds on a mathematical framework for the convergence analysis that ensures that the adaptive local learning rates of the Rprop’s schedule generate a descent search direction at each iteration. Simulation results in six problems of th...