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9781611970685 Academic Inspection Copy

Semismooth Newton Methods for Variational Inequalities and Constrained Optimization Problems in Function Spaces

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Semismooth Newton methods are a modern class of remarkably powerful and versatile algorithms for solving constrained optimization problems with partial differential equations (PDEs), variational inequalities and related problems. This book provides a comprehensive presentation of these methods in function spaces, choosing a balance between thoroughly developed theory and numerical applications. Although largely self-contained, the book also covers recent developments such as state-constrained problems and offers new material on topics such as improved mesh independence results. The theory and methods are applied to a range of practically important problems, including: * optimal control of nonlinear elliptic differential equations * obstacle problems * flow control of instationary Navier-Stokes fluids In addition, the author covers adjoint-based derivative computation and the efficient solution of Newton systems by multigrid and preconditioned iterative methods.
Michael Ulbrich is Professor and Chair of Mathematical Optimization in the Department of Mathematics at the Technische Universitaet Muenchen. His main research areas include numerical nonlinear optimization and its applications, optimal control with PDEs, and complementarity problems.
Notation; Preface; 1. Introduction; 2. Elements of finite-dimensional nonsmooth analysis; 3. Newton methods for semismooth operator equations; 4. Smoothing steps and regularity conditions; 5. Variational inequalities and mixed problems; 6. Mesh independence; 7. Trust-region globalization; 8. State-constrained and related problems; 9. Several applications; 10. Optimal control of incompressible Navier-Stokes flow; 11. Optimal control of compressible Navier-Stokes flow; Appendix; Bibliography; Index.
A comprehensive treatment of semismooth Newton methods in function spaces: from their foundations to recent progress in the field.
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