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1984 | Buch

Combinatorial Optimization for Undergraduates

verfasst von: L. R. Foulds

Verlag: Springer US

Buchreihe : Undergraduate Texts in Mathematics

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SUCHEN

Über dieses Buch

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 nature contained 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.

Inhaltsverzeichnis

Frontmatter

Techniques

Frontmatter
Chapter 0. Introduction to the Techniques of Combinatorial Optimization
Abstract
Optimization is concerned with finding the best (or optimal) solution to a problem. In this book we are concerned with problems that can be stated in an unambiguous way, usually in terms of mathematical notation and terminology. It is also assumed that the value of any solution to a given problem can be measured in a quantifiable way and this value can be compared with that of any other solution to the problem. Problems of this nature have been posed since the beginning of mankind One of the earliest is recorded by Virgil in his Aeneid where he relates the dilemma of Queen Dido, who was to be given all the land she could enclose in the hide of a bull. She cut the hide into thin strips and joining these together formed a semicircle within which she enclosed a sizeable portion of land with the Mediterranean coast as the diameter. Later Archimedes conjectured that her mathematical solution was optimal; that is, a semicircle is the curve of fixed length which will, together with a straight line, enclose the largest possible area. This conjecture can be proved using a branch of optimization called the calculus of variations.
L. R. Foulds
Chapter 1. Linear Programming and Extensions
Abstract
One of the areas of mathematics which has extensive use in combinatorial optimization is called linear programming (LP). It derives its name from the fact that the LP problem is an optimization problem in which the objective function and all the constraints are linear. Many real-world problems can be formulated in this way. Even more problems can be effectively approximated by an LP model. Also an LP solution method can be used as a subroutine in solving integer-programming problems (as indicated in Section 2.1) and certain nonlinear optimization problems.
L. R. Foulds
Chapter 2. Solution Techniques
Abstract
This chapter describes some of the basic solution techniques for combinatorial optimization problems—integer programming, dynamic programming, and heuristic problem solving. It begins with integer programming (IP), that is, the optimization of a linear function subject to a number of linear constraints, where the variables of the function must be integers. The elementary notions of two broad approaches to integer programming—enumerative techniques and cutting planes, are covered.
L. R. Foulds
Chapter 3. Optimization on Graphs and Networks
Abstract
Natural gas is soon to be available in Scenic Valley (SV). There are 10 towns in the SV area. One town, Geyser Town (GT), contains the wellhead. The SV Gas Company (SVGC) has the task of designing a supply system so that there is a path of pipes from GT to every other town. The manager of SVGC wishes to lay the smallest amount of pipe possible. His team of engineers have drawn up Table 3.1 showing the length of pipe (in kilometers) required for a connection between each pair of towns. Connections at points other than towns are prohibited. This is shown in Fig. 3.1, based on a map of SV.
L. R. Foulds

Applications

Frontmatter
Chapter 4. Some Applications of Combinatorial Optimization Techniques
Abstract
The Westminster Company has a problem. Their office space is too small and they are planning a new one-floor office building. The problem is how to answer the architect’s question: How do you want the various facilities to be laid out? We begin by listing and enumerating the facilities:
(1)
President’s office.
 
(2)
Space for President’s secretary/receptionist.
 
(3)
Typing pool.
 
(4)
Sales Manager’s office.
 
(5)
Production Manager’s office.
 
(6)
Distribution Manager’s office.
 
(7)
Area for clerks and telephone operator.
 
(8)
Boardroom.
 
(9)
Washroom/Cafeteria.
 
(10)
Storeroom.
 
(11)
Building grounds.
 
L. R. Foulds
Chapter 5. Appendix
L. R. Foulds
Backmatter
Metadaten
Titel
Combinatorial Optimization for Undergraduates
verfasst von
L. R. Foulds
Copyright-Jahr
1984
Verlag
Springer US
Electronic ISBN
978-1-4613-9511-9
Print ISBN
978-1-4613-9513-3
DOI
https://doi.org/10.1007/978-1-4613-9511-9