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Graphs, Algorithms, and
Optimization William Kocay, Donald L. Kreher |
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Graph theory offers a
rich source of problems and techniques for programming and data structure
development, as well as for understanding computing theory, including
NP-completeness and polynomial reduction. A comprehensive text, Graphs,
Algorithms, and Optimization features clear exposition on modern
algorithmic graph theory presented in a rigorous, yet approachable way. The
book covers major areas of graph theory including discrete optimization and
its connection to graph algorithms. The authors explore surface topology for
an intuitive point of view and include detailed discussions on linear
programming that emphasize graph theory problems useful in mathematics and
computer science. Many algorithms are provided along with the data structure
needed to program the algorithms
efficiently. The book also provides coverage on algorithms complexity
and efficiency, NP-completeness, linear optimization, and linear programming
and its relationship to graph algorithms. Written in an
accessible and informal style, this work covers nearly all areas of graph
theory. Graphs, Algorithms, and Optimization provides a modern
discussion on graph theory applicable to mathematics, computer science, and
crossover applications. Features: ·
Provides a thorough treatment of graph theory
along with data structures to show how algorithms can be programmed. ·
Includes three chapters on linear optimization,
which show how linear programming is related to graph theory. ·
Emphasizes the use of programming to solve
graph theory problems. ·
Presents all algorithms from a generic point of
view, usable with any programming language.
William Kocay is a member of the Computer Science Department at the University of Manitoba,
Canada. Donald L. Kreher is a professor of Mathematics at the Michigan
Technological University, Houghton, USA. |
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