Pipeline Implementations of Neumann-Neumann and Dirichlet-Neumann Waveform Relaxation Methods

This paper is concerned with the reformulation of Neumann-Neumann Waveform Relaxation (NNWR) methods and Dirichlet-Neumann Waveform Relaxation (DNWR) methods, a family of parallel space-time approaches to solving time-dependent PDEs. By changing the order of the operations, pipeline-parallel computation of the waveform iterates are possible without changing the final solution. The parallel efficiency and the increased communication cost of the pipeline implementation is presented, along with weak scaling studies to show the effectiveness of the pipeline NNWR and DNWR algorithms.Comment: 20 pages, 8 figure

Anti-chiral edge states in an exciton polariton strip

We present a scheme to obtain anti-chiral edge states in an exciton-polariton honeycomb lattice with strip geometry, where the modes corresponding to both edges propagate in the same direction. Under resonant pumping the effect of a polariton condensate with nonzero velocity in one linear polarization is predicted to tilt the dispersion of polaritons in the other, which results in an energy shift between two Dirac cones and the otherwise flat edge states become tilted. Our simulations show that due to the spatial separation from the bulk modes the edge modes are robust against disorder.Comment: 6 pages, 5 figure

Dirichlet-Neumann and Neumann-Neumann Waveform Relaxation for the Wave Equation

We present a Waveform Relaxation (WR) version of the Dirichlet-Neumann and Neumann-Neumann algorithms for the wave equation in space time. Each method is based on a non-overlapping spatial domain decomposition, and the iteration involves subdomain solves in space time with corresponding interface condition, followed by a correction step. Using a Laplace transform argument, for a particular relaxation parameter, we prove convergence of both algorithms in a finite number of steps for finite time intervals. The number of steps depends on the size of the subdomains and the time window length on which the algorithms are employed. We illustrate the performance of the algorithms with numerical results, and also show a comparison with classical and optimized Schwarz WR methods.Comment: 8 pages, 6 figures, presented in 22nd International conference on Domain Decomposition Methods, to appear in Domain Decomposition in Science and Engineering XXII, LNCSE, Springer-Verlag 201

G\mathcal{G}-SELC: Optimization by sequential elimination of level combinations using genetic algorithms and Gaussian processes

Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in the pharmaceutical industry. An efficient solution to this problem will help reduce the expenditure at the early stages of drug discovery. In an attempt to solve this problem, Mandal, Wu and Johnson [Technometrics 48 (2006) 273--283] proposed the SELC algorithm. Although powerful, it fails to extract substantial information from the data to guide the search efficiently, as this methodology is not based on any statistical modeling. The proposed approach uses Gaussian Process (GP) modeling to improve upon SELC, and hence named $\mathcal{G}$-SELC. The performance of the proposed methodology is illustrated using four and five dimensional test functions. Finally, we implement the new algorithm on a real pharmaceutical data set for finding a group of chemical compounds with optimal properties.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS199 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org