An Introduction to Mathematical Programming by Nathan Grieve (.ePUB)+
File Size: 31.4 MB
An Introduction to Mathematical Programming and Network Science: Examples with Theory and Python (Springer Undergraduate Texts in Mathematics and Technology) by Nathan Grieve
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Overview: This text provides a practical, hands-on introduction to the fundamental concepts of mathematical programming and network science. Particular emphasis is placed on linear programming, mathematical modelling and case studies, the implementation of the Simplex Method in Python, and classical techniques from nonlinear convex programming. The text also features a discussion of mathematical programming within the context of algebraic modelling languages. Further, it includes material on matrix games, decision analysis, multicriteria optimization and non-directed networks. Designed as an introductory resource for upper-level undergraduate and graduate students, the book assumes only a modest mathematical background. Readers who have completed a second course in linear algebra, multivariable calculus, and an introductory course in probability and statistics will find the more advanced portions of the text especially accessible. Researchers and professionals in mathematics, engineering, technology, economics, business, and other quantitatively oriented fields will also find this book a valuable reference. Readers with background and interest in computer programming are encouraged to give their own direct implementation of the Simplex Method in computer programming languages such as Python, C++ or Java. With this in mind, our presentation here of the Simplex Method, via the theory of Tucker Tableau, allows for an efficient and direct implementation in any of these programming languages. This is illustrated in Chapter 6 which includes a pragmatic implementation of Tucker Tableau and the Simplex Method within Python.
Genre: Non-Fiction > Educational
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