Parallel Computing for Large-Scale Optimization

大规模优化问题出现在各种重要的 科学，工程和经济应用，并展示 结构，允许通过算法解决方案，拥有高 平行度。 研究将继续在发展 的分解算法，适用于分布式解决方案， 这些问题以及在这些方法的实施和测试中， 各种并行体系结构，包括多计算机和 计算机科学系提供的多处理器和那些 通过ARPANET在其他地方提供。 这样的算法有很大的 粒度和低数据传输特性，非常适合 并行架构 将特别强调网络 优化问题，因为最困难的大比例 数学规划的实际应用就属于这种情况。 由于这些问题涉及的范围很广， 数学类（从线性到非线性，从凸到 非凸，从确定性到随机性）。 的发展 这些问题的并行算法也需要 开发调度和负载平衡技术， 在其他大型领域的分布式技术研究中非常有用- 规模科学计算。 问题的分解在多个上同时运行 如果要使这些新的体系结构 有效使用。

Large-scale optimization problems arise in a great variety of important scientific, engineering, and economic applications, and exhibit structures that allow solution via algorithms that possess a high degree of parallelism. Research will be continued in the development of decomposition algorithms appropriate for the distributed solution of such problems and in the implementation and testing of these methods on a variety of parallel architectures including the multicomputers and multiprocessors available at the Computer Sciences Department and those available elsewhere via ARPANET. Such algorithms have the large granularity and low data transfer properties that are ideally suited to parallel architectures. Particular emphasis will be given to network optimization problems, since a large proportion of the most difficult real-world applications of mathematical programming fall into this category, and since such problems also cover a broad spectrum of mathematical classes (from linear to nonlinear, from convex to nonconvex, from deterministic to stochatic). The development of parallel algorithms for these problems will also require the development of scheduling and load-balancing techniques that will be useful in the study of distributed techniques for other areas of large- scale scientific computation. The decomposition of problems to run simultaneously on multiple parallel processors is essential if these new architectures are to be effectively used.