OPTIMIZATION TECHNIQUES IN ENGINEERING: Foundations, Algorithms and Applications — From Classical Calculus to Metaheuristics and Machine-Learning-Assisted Design
Language: English
Published by Independently published, 2026
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
AbeBooks seller since October 27, 2023
Condition: New
£ 16.77
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- Title
- OPTIMIZATION TECHNIQUES IN ENGINEERING: Foundations, Algorithms and Applications — From Classical Calculus to Metaheuristics and Machine-Learning-Assisted Design
- Author
- Singh, Dr. Harmeet
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798187698622
- 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.
- The mathematics. The derivation, the conditions under which it is valid, and the convergence behaviour you should expect.
- The procedure. A step-by-step algorithm you could implement, in pseudocode or in Python, without further help.
- The engineering. A worked example drawn from a real discipline — a truss, a heat exchanger, a distribution network, a controller — carried through to numbers.
How the book is organised. The material is arranged in six parts.
- Part I — Foundations (Chapters 1–3) establishes vocabulary, the mathematical machinery of convexity and optimality, and — most neglected of all topics — the art of formulating an engineering problem so that it is solvable.
- Part II — Classical Deterministic Methods (Chapters 4–8) covers single-variable search, gradient-based multivariable methods, direct search, and the classical treatment of constraints through Lagrange multipliers, the Karush–Kuhn–Tucker conditions, penalty methods, and sequential quadratic programming.
- Part III — Linear, Network and Discrete Optimization (Chapters 9–13) develops linear programming and the simplex method, duality and sensitivity, network models, integer programming, and dynamic programming.
- Part IV — Metaheuristic and Nature-Inspired Methods (Chapters 14–17) treats genetic algorithms, swarm intelligence, simulated annealing, tabu search, differential evolution, and multi-objective optimization.
- Part V — Advanced Topics (Chapters 18–20) covers geometric and quadratic programming, optimization under uncertainty, and surrogate-based design.
- Part VI — Applications and Practice (Chapters 21–24) puts everything to work on structural, mechanical, electrical, chemical and civil case studies, surveys the software landscape, and closes with the fast-moving frontier where machine learning meets optimization.
"Synopsis" may belong to another edition of this title.
California Books
Miami, FL, U.S.A.
AbeBooks seller since October 27, 2023
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