Optimization Techniques in Engineering (Paperback)

Paperback. 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: 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.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.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.A note on rigour. I have not hidden the proofs, but I have not let them run the book either. Where a proof illuminates the algorithm - the orthogonality of successive steepest-descent directions, the geometric content of the KKT conditions, the reason the simplex method terminates - it is given in full. Where a proof is long and the result is intuitive, it is stated carefully and referenced. The engineer's task is to know what a theorem guarantees, not necessarily to re-derive it at three in the morning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.