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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Optimization Techniques in Engineering: Foundations, Algorithms and Applications - From Classical Calculus to Metaheuristics and Machine-Learning-Assi

Optimization Techniques in Engineering: Foundations, Algorithms and Applications - From Classical Calculus to Metaheuristics and Machine-Learning-Assi

Singh, Harmeet

Paperback

Automobiles

ISBN13: 9798187698622
Publisher: Independently Published
Published: Jul 17 2026
Pages: 346
Weight: 1.02
Height: 0.72 Width: 6.00 Depth: 9.00
Language: English
What the book tries to do. It tries to build, from first principles, an understanding of why optimization algorithms work - and, just as importantly, when they fail. Every algorithm in this book is presented in four layers:
  1. The idea. A plain-language statement of the trick that makes the method work, usually accompanied by a picture. If you remember nothing else from a chapter, remember the picture.
  2. The mathematics. The derivation, the conditions under which it is valid, and the convergence behaviour you should expect.
  3. The procedure. A step-by-step algorithm you could implement, in pseudocode or in Python, without further help.
  4. The engineering. A worked example drawn from a real discipline - a truss, a heat exchanger, a distribution network, a controller - carried through to numbers.
Who the book is for. The book is self-contained enough for a first course at the senior undergraduate level. It assumes calculus, linear algebra, and the patience to follow an algebraic argument; it does not assume prior exposure to operations research or to numerical analysis. Chapters 1-3 build the necessary background. For a postgraduate course, Chapters 7, 8, 12, 13, and 17-20 provide considerably more depth, and the later chapters on robust, stochastic and surrogate-assisted optimization take the reader to the edge of current practice.

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