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Titlebook: Large-Scale Scientific Computing; Third International Svetozar Margenov,Jerzy Waśniewski,Plamen Yalamov Conference proceedings 2001 Spring

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Java Communications for Large-Scale Parallel Computingffective use of Java in this area. In this article we present part of the results and proposed solutions to these problems. In particular, we report about the current status of the organized collaborations within the Java Grande Forum in the area of Message Passing for Java (MPJ) and faster Remote M
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Continuous Path Brownian Trajectories for Diffusion Monte Carlo via First- and Last-Passage Distributicular, we address three new strands of thought and their interaction with the GFFP method: the use of angle-averaging methods to reduce vector or tensor Laplace equations to scalar Laplace equations; the use of the simulation-tabulation (ST) method to dramatically expand the range of the GFFP meth
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Multilevel Monte Carlo Methodsd bythe author in [.] and present extensions and new developments of it. The tools needed for the convergence analysis of vector-valued Monte Carlo methods are discussed, as well. Applications to stochastic solution of integral equations are given for the case where an approximation of the full solu
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Sobolev Space Preconditioning for Mixed Nonlinear Elliptic Boundary Value Problemses the similar results of the authors for Dirichlet problems. Namely, a linear preconditioning operator is found first for the BVP itself on the continuous level, then the projection of this operator under the applied discretization will provide a natural preconditioning matrix. The mixed boundary c
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On a Schur Complement Approach for Solving Two-Level Finite Element Systemsoned iterative solution strategy, which makes use of the explicit form of the Schur complement system with respect to the coarse level degrees of freedom, is defined and compared with various direct and iterative solution methods.
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On-Line State Estimation of Maneuvering Objects by Sequential Monte Carlo Algorithmhe problem of tracking two maneuvering air targets in the presence of false alarms. The performance of the proposed algorithm is evaluated via Monte Carlo simulation. The results show that the nonlinear Bayesian filtering can be efficiently accomplished in real time by simple Monte Carlo techniques.
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