Parallelization of the discrete gradient method of non-smooth optimization and its applications

We investigate parallelization and performance of the discrete gradient method of nonsmooth optimization. This derivative free method is shown to be an effective optimization tool, able to skip many shallow local minima of nonconvex nondifferentiable objective functions. Although this is a sequential iterative method, we were able to parallelize critical steps of the algorithm, and this lead to a significant improvement in performance on multiprocessor computer clusters. We applied this method to a difficult polyatomic clusters problem in computational chemistry, and found this method to outperform other algorithms. <br /

Preparation of Logistics Managers for the Contemporary Environment of the European Union

This study reports and analyzes a questionnaire administered to US and European logistics managers soliciting their perceptions regarding changes in background and skill preferences for logistics managers operating in the new European Union environment. The combined sample of respondents appeared to indicate the importance of being a manager first and a functional/technical specialist second. While no statistically significant differences between the two subgroups were found with regard to background preferences, there were eight statistically significant differences between the two subgroups with regard to preferred skill requirements in the contemporary environment. Possible explanations for this phenomenon are suggested. Finally, implications of the survey findings for employers, practitioners, educators, and students are discussed