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Chapter 2 on linear programming is curiously missing from some of the material that the author has collected for later editions of this book. The chapter includes all of the material used in the book with the exception of the parts on parametric programming and model formulation. This is a serious omission because the chapter on parameterization can serve as a stand-alone LP course. On the other hand, parametric programming is not addressed at any other point in the text. The author does raise the important issue of identification. However, the theory of SMP and MPT is not adequately developed. Chapter 5 does not discuss the issues raised in 9.2.1 on robustness and the change of variables.

The efficient use of the algorithms may be off-putting to students. The author does not discuss the issue of time and space requirements. Exercises 1-12 are ones and zeros and therefore do not represent the optimal solution. For the smaller problems of Exercise 4, 5, 6, and 9, the optimal solution is a simplex never exceeding 0.5 and nonincreasing. I recommend a discussion of the merits of the LP problem in solving highly competitive problems.

The chapter is a good reference for students and instructors. It is appropriate for a student who is considering using LP, but it is lacking in specific information on course and research options. This book would be better suited to a student who has already chosen either courses or research topics in LP or an executive. Students can find this book particularly helpful because it addresses a common concern among LP students: \"How am I going to prove that I mathematically understand the procedures that one may use to solve a real-life problem?\" I recommend reading assignments 2, 3, and 6 at the beginning of the course. Solution to problems 8, 9, and 10 are good examples of how to use parametric programming. 7211a4ac4a