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Parallelization using the standard simplex algorithm

机译:使用标准单纯形算法进行并行化

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摘要

The simplex algorithm for linear programming has two major variants: the original, or standard method, and the revised method. Today, virtually all serious implementations are based on the revised method because it is much faster for sparse LPs, which are most common. However, the standard method has advantages as well. First, the standard method is effective for dense problems. While dense problems are uncommon in general, they occur frequently in some important applications such as wavelet decomposition, digital filter design, text categorization, and image processing. Second, the standard method can be easily and effectively extended to a coarse grained, distributed algorithm. We look at distributed linear programming especially optimized for loosely coupled workstations.
机译:线性规划的单纯形算法有两个主要变体:原始方法或标准方法,以及修订方法。如今,几乎所有严肃的实现都基于修订后的方法,因为对于稀疏LP(最常见的LP)而言,这种方法要快得多。但是,标准方法也具有优势。首先,标准方法对密集问题有效。虽然密集的问题通常并不常见,但在一些重要的应用程序中经常发生,例如小波分解,数字滤波器设计,文本分类和图像处理。其次,标准方法可以轻松有效地扩展到粗粒度的分布式算法。我们看一下专门为松耦合工作站优化的分布式线性编程。

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