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 |
Fingerprint
ASJC Scopus subject areas
- Engineering(all)
Cite this
SPARSE MATRIX TECHNIQUES FOR THE SHORTEST PATH PROBLEM. / Goto, Satoshi; Ohtsuki, Tatsuo; Yoshimura, Takeshi.
IEEE Trans Circuits Syst. Vol. CAS-23 12. ed. 1976. p. 752-758.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - SPARSE MATRIX TECHNIQUES FOR THE SHORTEST PATH PROBLEM.
AU - Goto, Satoshi
AU - Ohtsuki, Tatsuo
AU - Yoshimura, Takeshi
PY - 1976/12
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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0017219893&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0017219893&partnerID=8YFLogxK
M3 - Chapter
VL - CAS-23
SP - 752
EP - 758
BT - IEEE Trans Circuits Syst
ER -