Description
Introduction to the Mathematics of Operations Research with Mathematica® (2nd Ed.)
Author: Hastings Kevin J.
Language: EnglishSubjects for Introduction to the Mathematics of Operations Research...:
Keywords
Basic Feasible Solution; Non-basic Variables; Brownian motions; Simplex Algorithm; Markov chains; Objective Row; graph theory; Simplex Tableau; Poisson processes; Slack Variable; operations research; Initial Tableau; Spanning Tree; Transition Matrix; Adjacency Matrix; Recurrence Class; Profit Coefficient; Markov Chain; Undirected Graph; Maximal Flow Algorithm; Undeclared Variables; Final Tableau; Spanning Tree Algorithm; LP Problem; Connected Component; Vertex Labeling; Constraint Coefficients; Bipartite Graph; Directed Graph; Transition Diagram
Publication date: 09-2019
· 15.2x22.9 cm · Paperback
Publication date: 05-2006
500 p. · 15.2x22.9 cm · Hardback
Description
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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®, 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.