J
James B. Orlin
Researcher at Massachusetts Institute of Technology
Publications - 222
Citations - 24553
James B. Orlin is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Flow network & Minimum-cost flow problem. The author has an hindex of 58, co-authored 221 publications receiving 23587 citations. Previous affiliations of James B. Orlin include Stanford University & Bell Labs.
Papers
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Book
Network Flows: Theory, Algorithms, and Applications
TL;DR: In-depth, self-contained treatments of shortest path, maximum flow, and minimum cost flow problems, including descriptions of polynomial-time algorithms for these core models are presented.
Book
Network Flows
TL;DR: The question the authors are trying to ask is: how many units of water can they send from the source to the sink per unit of time?
Journal ArticleDOI
An STS-Based Map of the Human Genome
Thomas J. Hudson,Lincoln D. Stein,Sebastian S. Gerety,Junli Ma,Andrew B. Castle,James Silva,Donna K. Slonim,Rafael Baptista,Leonid Kruglyak,Shu-Hua Xu,Xintong Hu,Angela M. E. Colbert,Carl Rosenberg,Mary Pat Reeve-Daly,Steve Rozen,Lester Hui,Xiaoyun Wu,Christina Vestergaard,Kimberly M. Wilson,Jane S. Bae,Shanak Maitra,Soula Ganiatsas,Cheryl A. Evans,Margaret M. DeAngelis,Kimberly A. Ingalls,Robert Nahf,Lloyd T. Horton,Michele Oskin Anderson,Alville Collymore,Wenjuan Ye,Vardouhie Kouyoumjian,Irena S. Zemsteva,James P. Tam,Richard Devine,Dorothy F. Courtney,Michelle Turner Renaud,Huy Nguyen,Tara J. O'Connor,Cécile Fizames,Sabine Fauré,Gabor Gyapay,Colette Dib,Jean Morissette,James B. Orlin,Bruce W. Birren,Nathan Goodman,Jean Weissenbach,Trevor Hawkins,Simon J. Foote,David C. Page,Eric S. Lander +50 more
TL;DR: A physical map has been constructed of the human genome containing 15,086 sequence-tagged sites (STSs), with an average spacing of 199 kilobases, anchored by the radiation hybrid and genetic maps.
Journal ArticleDOI
A survey of very large-scale neighborhood search techniques
TL;DR: This paper surveys three broad classes of very large-scale neighborhood search (VLSN) algorithms: (1) variable-depth methods in which large neighbourhoods are searched heuristically, (2) large neighborhoods in which the neighborhoods are searched using network flow techniques or dynamic programming, and (3) large neighbourhoods induced by restrictions of the original problem that are solvable in polynomial time.
Book
Faster Algorithms for the Shortest Path Problem
TL;DR: Efficient implementations of Dijkstra's shortest path algorithm are investigated and a new data structure, called the radix heap, is proposed for use in this algorithm.