Combinatorial Optimization for Undergraduates

1. Front Matter

   Pages i-xii

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2. Techniques

   1. Front Matter

      Pages 1-1

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   2. Introduction to the Techniques of Combinatorial Optimization

      • L. R. Foulds

      Pages 3-11

   3. Linear Programming and Extensions

      • L. R. Foulds

      Pages 12-78

   4. Solution Techniques

      • L. R. Foulds

      Pages 79-117

   5. Optimization on Graphs and Networks

      • L. R. Foulds

      Pages 118-157

3. Applications

   1. Front Matter

      Pages 159-159

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   2. Some Applications of Combinatorial Optimization Techniques

      • L. R. Foulds

      Pages 161-201

   3. Appendix

      • L. R. Foulds

      Pages 202-215

4. Back Matter

   Pages 216-228

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The major purpose of this book is to introduce the main concepts of discrete optimization problems which have a finite number of feasible solutions. Following common practice, we term this topic combinatorial optimization. There are now a number of excellent graduate-level textbooks on combina­ torial optimization. However, there does not seem to exist an undergraduate text in this area. This book is designed to fill this need. The book is intended for undergraduates in mathematics, engineering, business, or the physical or social sciences. It may also be useful as a reference text for practising engineers and scientists. The writing of this book was inspired through the experience of the author in teaching the material to undergraduate students in operations research, engineering, business, and mathematics at the University of Canterbury, New Zealand. This experience has confirmed the suspicion that it is often wise to adopt the following approach when teaching material of the naturecontained in this book. When introducing a new topic, begin with a numerical problem which the students can readily understand; develop a solution technique by using it on this problem; then go on to general problems. This philosophy has been adopted throughout the book. The emphasis is on plausibility and clarity rather than rigor, although rigorous arguments have been used when they contribute to the understanding of the mechanics of an algorithm.

• Excel
• combinatorial optimization
• discrete optimization
• operations research
• optimization
• combinatorics

• Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA

  L. R. Foulds

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