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

TLDR
The author covers adjoint-based derivative computation and the efficient solution of Newton systems by multigrid and preconditioned iterative methods.
Abstract
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, striking a balance between thoroughly developed theory and numerical applications. Although largely self-contained, the book also covers recent developments in the field, 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 semilinear elliptic differential equations, obstacle problems, and 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. Audience: This book is appropriate for researchers and practitioners in PDE-constrained optimization, nonlinear optimization, and numerical analysis, as well as engineers interested in the current theory and methods for solving variational inequalities. It is also suitable as a text for an advanced graduate-level course in the aforementioned topics or applied functional analysis. Contents: Notation; Preface; Chapter One: Introduction; Chapter Two: Elements of Finite-Dimensional Nonsmooth Analysis; Chapter Three: Newton Methods for Semismooth Operator Equations; Chapter Four: Smoothing Steps and Regularity Conditions; Chapter Five: Variational Inequalities and Mixed Problems; Chapter Six: Mesh Independence; Chapter Seven: Trust-Region Globalization; Chapter Eight: State-Constrained and Related Problems; Chapter Nine: Several Applications; Chapter Ten: Optimal Control of Incompressible Navier-Stokes Flow; Chapter Eleven: Optimal Control of Compressible Navier-Stokes Flow; Appendix; Bibliography; Index.

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Citations
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Book

Implicit Functions and Solution Mappings: A View from Variational Analysis

TL;DR: In this paper, the authors define implicit functions defined implicitly by equations, and derive regularity properties of set-valued solution mappings through generalized derivatives, and apply them in Numerical Variational Analysis.
Journal ArticleDOI

Semismooth Newton Methods for Operator Equations in Function Spaces

TL;DR: A Newton-like method for nonsmooth operator equations is developed and its local q-superlinear convergence to regular solutions is proved, and the semismoothness of composite operators is established and corresponding chain rules are developed.
Journal ArticleDOI

A Bilevel Optimization Approach for Parameter Learning in Variational Models

TL;DR: This work considers a class of image denoising models incorporating $\ell_p$-norm--based analysis priors using a fixed set of linear operators and devise semismooth Newton methods for solving the resulting nonsmooth bilevel optimization problems.
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Proper Orthogonal Decomposition for Linear-Quadratic Optimal Control

TL;DR: The balanced truncation method as mentioned in this paper is based on transforming the state-space system into a balanced form so that its controllability and observability Gramians become diagonal and equal, and the states that are difficult to reach or to observe, are truncated.
Journal ArticleDOI

Regularization-robust preconditioners for time-dependent pde-constrained optimization problems ∗

TL;DR: This article motivates, derive, and test effective preconditioners to be used with the Minres algorithm for solving a number of saddle point systems which arise in PDE-constrained optimization problems.
References
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Book

Functional analysis

Walter Rudin
Book

Optimization and nonsmooth analysis

TL;DR: The Calculus of Variations as discussed by the authors is a generalization of the calculus of variations, which is used in many aspects of analysis, such as generalized gradient descent and optimal control.
Book

Finite Element Methods for Navier-Stokes Equations: Theory and Algorithms

TL;DR: This paper presents the results of an analysis of the "Stream Function-Vorticity-Pressure" Method for the Stokes Problem in Two Dimensions and its applications to Mixed Approximation and Homogeneous Stokes Equations.
Journal ArticleDOI

A limited memory algorithm for bound constrained optimization

TL;DR: An algorithm for solving large nonlinear optimization problems with simple bounds is described, based on the gradient projection method and uses a limited memory BFGS matrix to approximate the Hessian of the objective function.