Parallelization of the Discrete Gradient Method of Non-smooth Optimization and Its Applications

机译：非光滑优化离散梯度方法的并行化及其应用

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We investigate parallelization and performance of the discrete gradient method of norismooth 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.

机译：我们研究了Norismooth优化的离散梯度方法的并行化和性能。该无导数方法被证明是一种有效的优化工具，能够跳过非凸不可微目标函数的许多浅局部最小值。尽管这是一种顺序迭代方法，但是我们能够并行化算法的关键步骤，这导致了多处理器计算机群集上性能的显着提高。我们将该方法应用于计算化学中的难题多原子簇问题，发现该方法优于其他算法。

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《International Conference on Computational Science - ICCS 2003 Pt.3 Jun 2-4, 2003 Melbourne, Australia and St. Petersburg, Russia》|2003年|p.592-601|共10页
会议地点 Melbourne(AU) St. Petersburg(RU);Melbourne(AU) St. Petersburg(RU)
作者
G. Beliakov; J.E. Monsalve Tobon; A.M. Bagirov;
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School of Information Technology, Deakin University, 221 Burwood Hwy. Burwood 3125, Australia;

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原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
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Parallelization of the Discrete Gradient Method of Non-smooth Optimization and Its Applications

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