Engineering Optimization Theory and Practice – Singiresu S. Rao – 4th Edition


Now in its Fourth Edition, Professor Singiresu Rao’s acclaimed text Engineering Optimization enables readers to quickly master and apply all the important optimization methods in use today across a broad range of industries. Covering both the latest and classical optimization methods, the text starts off with the basics and then progressively builds to advanced principles and applications.

This comprehensive text covers nonlinear, linear, geometric, dynamic, and stochastic programming techniques as well as more specialized methods such as multiobjective, genetic algorithms, simulated annealing, neural networks, particle swarm optimization, ant colony optimization, and fuzzy optimization. Each method is presented in clear, straightforward language, making even the more sophisticated techniques easy to grasp. Moreover, the author provides:

  • Case examples that show how each method is applied to solve real-world problems across a variety of industries
  • Review questions and problems at the end of each chapter to engage readers in applying their newfound skills and knowledge
  • Examples that demonstrate the use of MATLAB® for the solution of different types of practical optimization problems
  • References and bibliography at the end of each chapter for exploring topics in greater depth
  • Answers to Review Questions available on the author’s Web site to help readers to test their understanding of the basic concepts

With its emphasis on problem-solving and applications, Engineering Optimization is ideal for upper-level undergraduates and graduate students in mechanical, civil, electrical, chemical, and aerospace engineering. In addition, the text helps practicing engineers in almost any industry design improved, more efficient systems at less cost.

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  • 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 Optimal Control and Optimality Criteria Methods.
    13 Modern Methods of Optimization.
    14 Practical Aspects of Optimization.

    A Convex and Concave Functions.
    B Some Computational Aspects of Optimization.
    C Introduction to MATLAB®.

  • Citation

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