SPARSE MATRIX TECHNIQUES FOR THE SHORTEST PATH PROBLEM.

Satoshi Goto; Tatsuo Ohtsuki; Takeshi Yoshimura

SPARSE MATRIX TECHNIQUES FOR THE SHORTEST PATH PROBLEM.

Satoshi Goto, Tatsuo Ohtsuki, Takeshi Yoshimura

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Two methods of implementing computer programs for solving the shortest path problem are presented. By symbolic processing, a computer program generates another program or an address table which represents an optimal shortest path algorithm, in the sense that only nontrivial operations required for a given particular network structure are executed. The implementation methods presented are powerful when a network of fixed sparseness structure must be solved repeatedly with different numerical values.

Original language	English
Title of host publication	IEEE Trans Circuits Syst
Pages	752-758
Number of pages	7
Volume	CAS-23
Edition	12
Publication status	Published - 1976 Dec
Externally published	Yes

ASJC Scopus subject areas

Engineering(all)

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Cite this

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title = "SPARSE MATRIX TECHNIQUES FOR THE SHORTEST PATH PROBLEM.",

abstract = "Two methods of implementing computer programs for solving the shortest path problem are presented. By symbolic processing, a computer program generates another program or an address table which represents an optimal shortest path algorithm, in the sense that only nontrivial operations required for a given particular network structure are executed. The implementation methods presented are powerful when a network of fixed sparseness structure must be solved repeatedly with different numerical values.",

author = "Satoshi Goto and Tatsuo Ohtsuki and Takeshi Yoshimura",

year = "1976",

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AU - Goto, Satoshi

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Y1 - 1976/12

N2 - Two methods of implementing computer programs for solving the shortest path problem are presented. By symbolic processing, a computer program generates another program or an address table which represents an optimal shortest path algorithm, in the sense that only nontrivial operations required for a given particular network structure are executed. The implementation methods presented are powerful when a network of fixed sparseness structure must be solved repeatedly with different numerical values.

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