NUST Institutions Library Catalogue

"A Wiley-Interscience publication."

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Includes bibliographical references and index.

1. Introduction to Optimization -- 2. Classical Optimization Techniques -- 3. Linear Programming I: Simplex Method -- 4. Linear Programming II: Additional Topics and Extensions -- 5. Nonlinear Programming I: One-Dimensional Minimization Methods -- 6. Nonlinear Programming II: Unconstrained Optimization Techniques -- 7. Nonlinear Programming III: Constrained Optimization Techniques -- 8. Geometric Programming -- 9. Dynamic Programming -- 10. Integer Programming -- 11. Stochastic Programming -- 12. Further Topics in Optimization -- 13. Practical Aspects of Optimization -- Appendix A Convex and Concave Functions -- Appendix B Some Computational Aspects of Optimization.

A rigorous mathematical approach to identifying a set of design alternatives and selecting the best candidate from within that set, engineering optimization was developed as a means of helping engineers to design systems that are both more efficient and less expensive and to develop new ways of improving the performance of existing systems.

Thanks to the breathtaking growth in computer technology that has occurred over the past decade, optimization techniques can now be used to find creative solutions to larger, more complex problems than ever before. As a consequence, optimization is now viewed as an indispensable tool of the trade for engineers working in many different industries, especially the aerospace, automotive, chemical, electrical, and manufacturing industries.

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In Engineering Optimization, Professor Singiresu S. Rao provides an application-oriented presentation of the full array of classical and newly developed optimization techniques now being used by engineers in a wide range of industries.

Essential proofs and explanations of the various techniques are given in a straightforward, user-friendly manner, and each method is copiously illustrated with real-world examples that demonstrate how to maximize desired benefits while minimizing negative aspects of project design.

Comprehensive, authoritative, up-to-date, Engineering Optimization provides in-depth coverage of linear and nonlinear programming, dynamic programming, integer programming, and stochastic programming techniques as well as several breakthrough methods, including genetic algorithms, simulated annealing, and neural network-based and fuzzy optimization techniques.

Designed to function equally well as either a professional reference or a graduate-level text, Engineering Optimization features many solved problems taken from several engineering fields, as well as review questions, important figures, and helpful references.

Engineering Optimization is a valuable working resource for engineers employed in practically all technological industries. It is also a superior didactic tool for graduate students of mechanical, civil, electrical, chemical, and aerospace engineering.