Introduction to the mathematics of operations research with Mathematica
Author(s)
Bibliographic Information
Introduction to the mathematics of operations research with Mathematica
（Monographs and textbooks in pure and applied mathematics, [128]）
Chapman & Hall/CRC, 2006
2nd ed
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Introduction to the mathematics of operations research
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
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Note
First ed. published in 1989 by M. Dekker under the title: Introduction to the mathematics of operations research
On spine: 279
Includes bibliographical references (p. 558-561) and index
Description and Table of Contents
Description
The breadth of information about operations research and the overwhelming size of previous sources on the subject make it a difficult topic for non-specialists to grasp. Fortunately, Introduction to the Mathematics of Operations Research with Mathematica (R), Second Edition delivers a concise analysis that benefits professionals in operations research and related fields in statistics, management, applied mathematics, and finance.
The second edition retains the character of the earlier version, while incorporating developments in the sphere of operations research, technology, and mathematics pedagogy. Covering the topics crucial to applied mathematics, it examines graph theory, linear programming, stochastic processes, and dynamic programming. This self-contained text includes an accompanying electronic version and a package of useful commands. The electronic version is in the form of Mathematica notebooks, enabling you to devise, edit, and execute/reexecute commands, increasing your level of comprehension and problem-solving.
Mathematica sharpens the impact of this book by allowing you to conveniently carry out graph algorithms, experiment with large powers of adjacency matrices in order to check the path counting theorem and Markov chains, construct feasible regions of linear programming problems, and use the "dictionary" method to solve these problems. You can also create simulators for Markov chains, Poisson processes, and Brownian motions in Mathematica, increasing your understanding of the defining conditions of these processes. Among many other benefits, Mathematica also promotes recursive solutions for problems related to first passage times and absorption probabilities.
Table of Contents
Graph Theory and Network Analysis. Linear Programming. Further Topics in Linear Programming. Markov Chains. Continuous Time Processes. Dynamic Programming.
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