Engineering Design OptimizationCambridge University Press, 2021 M11 18 - 637 páginas Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments. |
Contenido
A Short History of Optimization | 33 |
Numerical Models and Solvers | 47 |
Unconstrained GradientBased Optimization | 79 |
Constrained GradientBased Optimization | 153 |
Computing Derivatives | 223 |
GradientFree Optimization | 281 |
Discrete Optimization | 327 |
Multiobjective Optimization | 355 |
Optimization Under Uncertainty | 441 |
Multidisciplinary Design Optimization | 475 |
A Mathematics Background | 539 |
A 8 | 553 |
B Linear Solvers | 559 |
QuasiNewton Methods | 571 |
Test Problems | 579 |
Bibliography | 591 |
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Términos y frases comunes
adjoint analytic approach approximation Chapter cited components constrained problem convergence convex convex optimization corresponding coupled system coupling variables curvature defined design optimization design variables differentiation dimensions discrete distribution equality constraints equations error evaluations example feasible finite differences formulation function value global global optimum gradient gradient-based optimization gradient-free Hessian implicit inequality constraints inputs iteration Jacobian kriging Lagrange multipliers Lagrangian line search linear system mathematical matrix minimize minimum multidisciplinary multiple Newton Newton's method node nonlinear objective and constraint objective function one-dimensional optimization algorithms optimization problem optimum outputs parameter partial derivatives penalty polynomial programming quadratic quadrature quasi-Newton quasi-Newton methods require residual Rosenbrock function sampling search direction Section sequence shown in Fig solution solve solver space step structure subproblem surrogate model Taylor series tion total derivatives trust region trust-region update vector Wolfe conditions Xk+1 yields zero