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

Garng Morton 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 - 1 Jan 1995
Externally publishedYes

Fingerprint

Parallel algorithms
Decomposition

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

Cite this

Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems. / Huang, Garng Morton; Hsieh, Wen Lin.

In: IEEE Transactions on Parallel and Distributed Systems, Vol. 6, No. 11, 01.01.1995, p. 1132-1146.

Research output: Contribution to journalArticle

@article{48734416ca07494dbfcb3b1e9e5efe8e,
title = "Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems",
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.",
keywords = "Exact convergence, Kuhn-Tucker theorem, optimal routing, parallel processing, textured algorithm",
author = "Huang, {Garng Morton} and Hsieh, {Wen Lin}",
year = "1995",
month = "1",
day = "1",
doi = "10.1109/71.476185",
language = "English",
volume = "6",
pages = "1132--1146",
journal = "IEEE Transactions on Parallel and Distributed Systems",
issn = "1045-9219",
publisher = "IEEE Computer Society",
number = "11",

}

TY - JOUR

T1 - Exact Convergence of a Parallel Textured Algorithm for Data Network Optimal Routing Problems

AU - Huang, Garng Morton

AU - Hsieh, Wen Lin

PY - 1995/1/1

Y1 - 1995/1/1

N2 - 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.

AB - 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.

KW - Exact convergence

KW - Kuhn-Tucker theorem

KW - optimal routing

KW - parallel processing

KW - textured algorithm

UR - http://www.scopus.com/inward/record.url?scp=0029405962&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029405962&partnerID=8YFLogxK

U2 - 10.1109/71.476185

DO - 10.1109/71.476185

M3 - Article

VL - 6

SP - 1132

EP - 1146

JO - IEEE Transactions on Parallel and Distributed Systems

JF - IEEE Transactions on Parallel and Distributed Systems

SN - 1045-9219

IS - 11

ER -