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A new type of overlapping Schwarz methods, using discontinuous iterates, is constructed by modifying the classical overlapping Schwarz algorithm. This new algorithm allows for discontinuous iterates across the artificial interface. For Poisson's equation, this algorithm can be considered as an overlapping version of Lions' Robin iteration method for which little is known concerning the rate of convergence. Since overlap improves the performance of the classical algorithms considerably, the existence of a uniform convergence factor is the fundamental question for our algorithm. A new theory using Lagrange multipliers is developed and conditions are found for the existence of an almost uniform convergence factor for the dual variables, which implies rapid convergence of the primal variables, in the two overlapping subdomain case. Our result also shows a relation between the boundary conditions of the given problem and the artificial interface condition. Numerical results for the general case with cross points are also presented. They indicate possible extensions of our results to this more general case. (2000): 65F10, 65N30, 65N55 Mathematics Subject Classification

We study a space-time finite element approach for the nonhomogeneous wave equation using a continuous time Galerkin method. We present fully implicit examples in 1+1, 2+1, and 3+1 dimensions using linear quadrilateral, hexahedral, and tesseractic elements. Krylov solvers with additive Schwarz preconditioning are used for solving the linear system. We introduce a time decomposition strategy in preconditioning which significantly improves performance when compared with unpreconditioned cases.

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respectively. The financial support from Agricultural Experiment Station at South Dakota State University (Project H363-10) and Stahly Scholar in Financial Economics are gratefully acknowledged by Wang. We appreciate the comments by Wade Brorsen and Philip Garcia and other participants at the NCCC-134 conference. A Jump Diffusion Model for Agricultural Commodities with Bayesian Analysis Stochastic volatility, price jumps, seasonality, and stochastic cost of carry, have been included separately, but not collectively, in pricing models of agricultural commodity futures and options. We propose a comprehensive model that incorporates all four features. We employ a special Markov Chain Monte Carlo algorithm, new in the agricultural commodity derivatives pricing literature, to estimate the proposed stochastic volatility (SV) and stochastic volatility with jumps (SVJ) models. Overall model fitness tests favor the SVJ model. The in-sample and out-of-sample pricing and hedging results for corn, soybeans and wheat generally, with few exceptions, lend support for the SVJ model.