• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Advances in Engineering Software >Optimised sparse storage mode for symbolic analysis of large networks
【24h】

Optimised sparse storage mode for symbolic analysis of large networks

机译：优化的稀疏存储模式，用于大型网络的符号分析

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Symbolic network analysis gained growing interest as it aims at producing outputs in the form of expressions that containing both variables and numbers. However, such analysis faces the primary difficulty of the exponential growth of product terms in a symbolic network function with respect to circuit size. This long-standing difficulty is only partially overcome by various symbolic approximations and hierarchical decomposition approaches. A new storage scheme called Row-Indexed Semi-Symmetric Sparse (RISS) storage mode that partially solves this difficulty is presented in this paper. Unlike other similar storage schemes, the proposed scheme requires only about twice the number of nonzero matrix elements at most. The efficiency of the proposed RISS storage mode is assessed by considering several matrices of moderate sizes and comparing the memory requirement for each matrix in full storage mode and in RISS storage mode. The overall performance of a solver that incorporates the RISS storage mode and the sparse matrix techniques is assessed by considering a typical example of a 90 degrees phase splitting network. When compared to an alternative matrix solver based on successive matrix reduction, the proposed solver demonstrates a reduction of 65% in the operation count and a reduction of 60% in the average memory storage requirement. (C) 2006 Published by Elsevier Ltd.
机译：符号网络分析越来越引起人们的兴趣，因为它旨在产生既包含变量又包含数字的表达式形式的输出。但是，这样的分析面临符号网络功能中乘积项相对于电路大小呈指数增长的主要困难。这种长期存在的困难只能通过各种符号近似和层次分解方法来部分克服。本文提出了一种新的存储方案，称为行索引半对称稀疏（RISS）存储模式，可以部分解决此难题。与其他类似的存储方案不同，所提出的方案最多仅需要大约非零矩阵元素数量的两倍。通过考虑几种中等大小的矩阵并比较完全存储模式和RISS存储模式下每个矩阵的内存要求，可以评估所提出的RISS存储模式的效率。结合了RISS存储模式和稀疏矩阵技术的求解器的整体性能通过考虑90度分相网络的典型示例进行评估。与基于连续矩阵约简的替代矩阵求解器相比，拟议的求解器证明运算次数减少了65％，平均内存存储需求减少了60％。 （C）2006由Elsevier Ltd.出版

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号