Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems

Garng M. Huang, Wen Lin Hsieh

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In our earlier paper [1], a textured decomposition based algorithm is developed to solve the optimal routing problem in data networks; a few examples were used to illustrate the speedup advantage and the convergence conditions for the textured algorithm to converge to a global minimum. The speedup advantage is investigated in [2]. However, the theoretical foundation is not provided. In this paper, we provide the foundation. First, we show that for any textured decomposition, the algorithm always converges to a stationary point, which may not be a global minimum. And then, we prove that if the conditions of the exact convergence theorem are satisfied, the textured algorithm will converge to a global minimum.

Original languageEnglish
Pages (from-to)1132-1146
Number of pages15
JournalIEEE Transactions on Parallel and Distributed Systems
Volume6
Issue number11
DOIs
Publication statusPublished - Nov 1995

Keywords

  • Exact convergence
  • Kuhn-Tucker theorem
  • optimal routing
  • parallel processing
  • textured algorithm

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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