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EN
In our work, an agent-based system supporting workers in an organization is centered around utilization of ontologically demarcated data. In this system, ontological matchmaking, understood as a way of establishing closeness between resources, is one of key functionalities. Specifically, it is used to autonomously provide recommendations to the user, who is represented by her/his personal agent. These recommendations specify, which among available resources are relevant / of interest to the worker. In this paper, we discuss our approach to measuring semantic closeness between ontologically demarcated information objects, while a Duty Trip Support application is used as a case study. General description of the algorithm is followed by a recommendation example based on support for a worker who is seeking advice in planning a duty trip.
2
Content available remote Recent advances in solvers for nonlinear algebraic equations
EN
In this paper the performance of four solvers for systems of nonlinear algebraic equations applied to a number of test problems with up to 250 equations is discussed. These problems have been collected from research papers and from the Internet and are often recognized as ``standard'' tests. Solver quality is assessed by studying their convergence and sensitivity to simple starting vectors. Experimental data is also used to categorize the test problems themselves. Future research directions are summarized.
3
Content available remote A fast solver for the complex symmetric eigenproblem
EN
Many numerical methods for studying chemical reaction problems require the computation of the eigenvalues of very large complex symetric matrices. Recently, a new algorithm for this problem has been proposed by Bar-On and Ryaboy [3]. This algorithm is similar in concept and complexity to the Hermitian eigensolver and is based on application of complex orthogonal transformations to preserve symmetry and recovery transformations to preserve stability. We demonstrate the performance of the proposed algorithm on several high performance computers from Digital, SGI, and Cray . The results show that the new algorithm is much faster than the general eigensolver, the present method used for solving these problems.
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