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Adaptive and compact discretization for weighted region optimal path finding

Publication ,  Journal Article
Sun, Z; Reif, JH
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2003

This paper presents several results on the weighted region optimal path problem. An often-used approach to approximately solve this problem is to apply a discrete search algorithm to a graph Gε generated by a discretization of the problem; this graph guarantees to contain an ε-approximation of an optimal path between given source and destination points. We first provide a discretization scheme such that the size of Gε does not depend on the ratio between the maximum and minimum unit weights. This leads to the first ε-approximation algorithm whose complexity is not dependent on the unit weight ratio. We also introduce an empirical method, called adaptive discretization method, that improves the performance of the approximation algorithms by placing discretization points densely only in areas that may contain optimal paths. BUSHWHACK is a discrete search algorithm used for finding optimal paths in Gε. We added two heuristics to BUSHWHACK to improve its performance and scalability. © Springer-Verlag Berlin Heidelberg 2003.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2003

Volume

2751

Start / End Page

258 / 270

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Sun, Z., & Reif, J. H. (2003). Adaptive and compact discretization for weighted region optimal path finding. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2751, 258–270. https://doi.org/10.1007/978-3-540-45077-1_24
Sun, Z., and J. H. Reif. “Adaptive and compact discretization for weighted region optimal path finding.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2751 (January 1, 2003): 258–70. https://doi.org/10.1007/978-3-540-45077-1_24.
Sun Z, Reif JH. Adaptive and compact discretization for weighted region optimal path finding. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2003 Jan 1;2751:258–70.
Sun, Z., and J. H. Reif. “Adaptive and compact discretization for weighted region optimal path finding.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2751, Jan. 2003, pp. 258–70. Scopus, doi:10.1007/978-3-540-45077-1_24.
Sun Z, Reif JH. Adaptive and compact discretization for weighted region optimal path finding. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2003 Jan 1;2751:258–270.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2003

Volume

2751

Start / End Page

258 / 270

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences