Multilevel Coding Schemes for Compute-and-Forward

We investigate techniques for designing modulation/coding schemes for the wireless two-way relaying channel. The relay is assumed to have perfect channel state information, but the transmitters are assumed to have no channel state information. We consider physical layer network coding based on multilevel coding techniques. Our multilevel coding framework is inspired by the compute-and-forward relaying protocol. Indeed, we show that the framework developed here naturally facilitates decoding of linear combinations of codewords for forwarding by the relay node. We develop our framework with general modulation formats in mind, but numerical results are presented for the case where each node transmits using the QPSK constellation with gray labeling. We focus our discussion on the rates at which the relay may reliably decode linear combinations of codewords transmitted from the end nodes

Multilevel Accelerated Quadrature for PDEs with Log-Normally Distributed Diffusion Coefficient

This article is dedicated to multilevel quadrature methods for the rapid solution of stochastic partial differential equations with a log-normally distributed diffusion coefficient. The key idea of such approaches is a sparse-grid approximation of the occurring product space between the stochastic and the spatial variable. We develop the mathematical theory and present error estimates for the computation of the solution's moments with focus on the mean and the variance. Especially, the present framework covers the multilevel Monte Carlo method and the multilevel quasi-Monte Carlo method as special cases. The theoretical findings are supplemented by numerical experiments. This article is dedicated to multilevel quadrature methods forthe rapid solution of stochastic partial differential equationswith a log-normally distributed diffusion coefficient. The key ideaof such approaches is a sparse-grid approximation of the occurring product space between the stochastic and the spatial variable. We develop the mathematical theory and present error estimates for the computation of the solution's moments with focus on the mean and the variance. Especially, the present framework covers the multilevel Monte Carlo method and the multilevel quasi-Monte Carlo method as special cases. The theoretical findings are supplemented by numerical experiments

Multilevel Markov Chain Monte Carlo Method for High-Contrast Single-Phase Flow Problems

In this paper we propose a general framework for the uncertainty quantification of quantities of interest for high-contrast single-phase flow problems. It is based on the generalized multiscale finite element method (GMsFEM) and multilevel Monte Carlo (MLMC) methods. The former provides a hierarchy of approximations of different resolution, whereas the latter gives an efficient way to estimate quantities of interest using samples on different levels. The number of basis functions in the online GMsFEM stage can be varied to determine the solution resolution and the computational cost, and to efficiently generate samples at different levels. In particular, it is cheap to generate samples on coarse grids but with low resolution, and it is expensive to generate samples on fine grids with high accuracy. By suitably choosing the number of samples at different levels, one can leverage the expensive computation in larger fine-grid spaces toward smaller coarse-grid spaces, while retaining the accuracy of the final Monte Carlo estimate. Further, we describe a multilevel Markov chain Monte Carlo method, which sequentially screens the proposal with different levels of approximations and reduces the number of evaluations required on fine grids, while combining the samples at different levels to arrive at an accurate estimate. The framework seamlessly integrates the multiscale features of the GMsFEM with the multilevel feature of the MLMC methods following the work in \cite{ketelson2013}, and our numerical experiments illustrate its efficiency and accuracy in comparison with standard Monte Carlo estimates.Comment: 29 pages, 6 figure

Managing pollution control in Brazil : the potential use of taxes and fines by federal and state governments

The authors make a case for federal monitoring of state environmental agencies'(SEPAs') performance because of the tradeoff for the states between the need to raise revenue from taxes on local output and the need to limit pollution. They also show that fines and taxes assigned respectively to the federal and state governments can improve firms'compliance and SEPA's performance, and hence environmental quality, without damaging state revenue, and perhaps even improving it. For their analysis, the authors rely on numerical policy simulations based on an analytical framework designed as a multilevel Stackelberg game. This framework reproduces the hierarchical structure of pollution control policies in Brazil, where the federal environmental protection agency relies on SEPAs to ensure that federally defined minimum ambient standards are met locally. The numerical simulations are based on a case study of the food, and the printing and publishing industries.Urban Services to the Poor,Environmental Economics&Policies,Water and Industry,Pollution Management&Control,Health Monitoring&Evaluation

Multilevel ensemble Kalman filtering for spatio-temporal processes

We design and analyse the performance of a multilevel ensemble Kalman filter method (MLEnKF) for filtering settings where the underlying state-space model is an infinite-dimensional spatio-temporal process. We consider underlying models that needs to be simulated by numerical methods, with discretization in both space and time. The multilevel Monte Carlo (MLMC) sampling strategy, achieving variance reduction through pairwise coupling of ensemble particles on neighboring resolutions, is used in the sample-moment step of MLEnKF to produce an efficient hierarchical filtering method for spatio-temporal models. Under sufficient regularity, MLEnKF is proven to be more efficient for weak approximations than EnKF, asymptotically in the large-ensemble and fine-numerical-resolution limit. Numerical examples support our theoretical findings.Comment: Version 1: 39 pages, 4 figures.arXiv admin note: substantial text overlap with arXiv:1608.08558 . Version 2 (this version): 52 pages, 6 figures. Revision primarily of the introduction and the numerical examples sectio

Smolyak's algorithm: A powerful black box for the acceleration of scientific computations

We provide a general discussion of Smolyak's algorithm for the acceleration of scientific computations. The algorithm first appeared in Smolyak's work on multidimensional integration and interpolation. Since then, it has been generalized in multiple directions and has been associated with the keywords: sparse grids, hyperbolic cross approximation, combination technique, and multilevel methods. Variants of Smolyak's algorithm have been employed in the computation of high-dimensional integrals in finance, chemistry, and physics, in the numerical solution of partial and stochastic differential equations, and in uncertainty quantification. Motivated by this broad and ever-increasing range of applications, we describe a general framework that summarizes fundamental results and assumptions in a concise application-independent manner