A rigorous yet accessible textbook covering both fundamental and advanced optimization topics. Covering both gradient-based and gradient-free algorithms, derivative computation, and numerous visualizations, examples and problems, it is ideal for graduate courses on optimization in aerospace, civil, and mechanical engineering departments.
A rigorous yet accessible textbook covering both fundamental and advanced optimization topics. Covering both gradient-based and gradient-free algorithms, derivative computation, and numerous visualizations, examples and problems, it is ideal for graduate courses on optimization in aerospace, civil, and mechanical engineering departments.
Joaquim R. R. A. Martins is a Professor of Aerospace Engineering at the University of Michigan. He is a fellow of the American Institute for Aeronautics and Astronautics, and the Royal Aeronautical Society.
Inhaltsangabe
1. Introduction 2. A short history of optimization 3. Numerical models and solvers 4. Unconstrained gradient-based optimization 5. Constrained gradient-based optimization 6. Computing derivatives 7. Gradient-free optimization 8. Discrete optimization 9. Multiobjective optimization 10. Surrogate-based optimization 11. Convex optimization 12. Optimization under uncertainity 13. Multidisciplinary design optimization A. Mathematics background B. Linear solvers C. Quasi-Newton methods D. Test problems.
1. Introduction 2. A short history of optimization 3. Numerical models and solvers 4. Unconstrained gradient-based optimization 5. Constrained gradient-based optimization 6. Computing derivatives 7. Gradient-free optimization 8. Discrete optimization 9. Multiobjective optimization 10. Surrogate-based optimization 11. Convex optimization 12. Optimization under uncertainity 13. Multidisciplinary design optimization A. Mathematics background B. Linear solvers C. Quasi-Newton methods D. Test problems.
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