Algorithms for Optimization provides a comprehensive and practical introduction to optimization algorithms, with an emphasis on engineering design problems. The book begins with derivatives, gradients, bracketing, local descent, and first- and second-order optimization methods, then introduces direct, stochastic, and population-based methods. It covers constrained and linear constrained optimization, multiobjective optimization, sampling plans, surrogate and probabilistic surrogate models, and optimization under uncertainty. Later chapters address uncertainty propagation, discrete optimization, expression optimization, and multidisciplinary optimization. The book combines mathematical explanations with figures, examples, exercises, and concrete implementations in the Julia programming language. Appendixes introduce Julia, test functions, and mathematical concepts used to analyze optimization methods, making the book useful for advanced undergraduate and graduate students as well as professionals in engineering, mathematics, statistics, computer science, and operations research.

Ngôn ngữ:	eng
Tác giả:	Mykel J. Kochenderfer, Tim A. Wheeler
Thông tin nhan đề:	Algorithms for Optimization
Nhà xuất bản:	The MIT Press
Loại hình:	Giáo trình sách, tham khảo / Bộ sưu tập: Công nghệ thông tin
Mô tả vật lý:	500tr
Năm xuất bản:	2019