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Real-Time PDE-Constrained Optimization illustrated edition [Pehme köide]

  • Formaat: Paperback / softback, 335 pages, kõrgus x laius x paksus: 229x152x21 mm, kaal: 585 g, Illustrations (some col.)
  • Sari: Computational Science & Engineering No. 3
  • Ilmumisaeg: 30-Apr-2007
  • Kirjastus: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 0898716217
  • ISBN-13: 9780898716214
Teised raamatud teemal:
  • Formaat: Paperback / softback, 335 pages, kõrgus x laius x paksus: 229x152x21 mm, kaal: 585 g, Illustrations (some col.)
  • Sari: Computational Science & Engineering No. 3
  • Ilmumisaeg: 30-Apr-2007
  • Kirjastus: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 0898716217
  • ISBN-13: 9780898716214
Teised raamatud teemal:
Partial differential equations, the workhorses of the engineering and scientific worlds, are singular and significant elements of design, control and parameter estimation. However, their potential complexity can make it impossible to achieve rapid solutions, particularly in the case of time-sensitive applications such as simulation-based decision making. Contributed by practitioners and academics, this focuses on new formulations, methods and algorithms researchers and engineers need to optimize PDE-controlled situations. Topics include the constrained optimal feedback control of systems governed by large differential algebraic equations , a stabilizing real-time implementation of nonlinear model prediction control, numerical feedback controller design, a least-squares finite element method, a collection of fast PDE-constrained optimization solvers, recommendations for reduced-order modeling and a range of applications. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

“…a timely contribution to a field of growing importance. This carefully edited book presents a rich collection of chapters ranging from mathematical methodology to emerging applications. I recommend it to students as a rigorous and comprehensive presentation of simulation-based optimization and to researchers as an overview of recent advances and challenges in the field.” — Jorge Nocedal, Professor, Northwestern University.Many engineering and scientific problems in design, control, and parameter estimation can be formulated as optimization problems that are governed by partial differential equations (PDEs). The complexities of the PDEs—and the requirement for rapid solution—pose significant difficulties. A particularly challenging class of PDE-constrained optimization problems is characterized by the need for real-time solution, i.e., in time scales that are sufficiently rapid to support simulation-based decision making. Real-Time PDE-Constrained Optimization, the first book devoted to real-time optimization for systems governed by PDEs, focuses on new formulations, methods, and algorithms needed to facilitate real-time, PDE-constrained optimization. In addition to presenting state-of-the-art algorithms and formulations, the text illustrates these algorithms with a diverse set of applications that includes problems in the areas of aerodynamics, biology, fluid dynamics, medicine, chemical processes, homeland security, and structural dynamics. Despite difficulties, there is a pressing need to capitalize on continuing advances in computing power to develop optimization methods that will replace simple rule-based decision making with optimized decisions based on complex PDE simulations. Audience The book is aimed at readers who have expertise in simulation and are interested in incorporating optimization into their simulations, who have expertise in numerical optimization and are interested in adapting optimization methods to the class of infinite-dimensional simulation problems, or who have worked in “offline” optimization contexts and are interested in moving to “online” optimization.Contents Preface; Part I: Concepts and Properties of Real-Time, Online Strategies. Chapter 1: Constrained Optimal Feedback Control of Systems Governed by Large Differential Algebraic Equations; Chapter 2: A Stabilizing Real-Time Implementation of Nonlinear Model Predictive Control; Chapter 3: Numerical Feedback Controller Design for PDE Systems Using Model Reduction: Techniques and Case Studies; Chapter 4: Least-Squares Finite Element Method for Optimization and Control Problems; Part II: Fast PDE-Constrained Optimization Solvers. Chapter 5: Space-Time Multigrid Methods for Solving Unsteady Optimal Control Problems; Chapter 6: A Time-Parallel Implicit Methodology for the Near-Real-Time Solution of Systems of Linear Oscillators; Chapter 7: Generalized SQP Methods with “Parareal” Time-Domain Decomposition for Time-Dependent PDE-Constrained Optimization; Chapter 8: Simultaneous Pseudo-Timestepping for State-Constrained Optimization Problems in Aerodynamics; Chapter 9: Digital Filter Stepsize Control in DASPK and Its Effect on Control Optimization Performance; Part III: Reduced Order Modeling. Chapter 10: Certified Rapid Solution of Partial Differential Equations for Real-Time Parameter Estimation and Optimization; Chapter 11: Model Reduction for Large-Scale Applications in Computational Fluid Dynamics; Chapter 12: Suboptimal Feedback Control of Flow Separation by POD Model Reduction; Part IV: Applications. Chapter 13: A Combined Shape-Newton and Topology Optimization Technique in Real-Time Image Segmentation; Chapter 14: COFIR: Coarse and Fine Image Registration; Chapter 15: Real-Time, Large Scale Optimization of Water Network Systems Using a Sub-domain Approach; Index.
Preface xvii
I. Concepts and Properties of Real-Time, Online Strategies
1(94)
Constrained Optimal Feedback Control of Systems Governed by Large Differential Algebraic Equations
3(22)
Hans Georg Bock
Moritz Diehl
Ekaterina Kostina
Johannes P. Schloder
Introduction
3(3)
Differential Algebraic Equation Systems
4(1)
Nonlinear Model Predictive Control
4(1)
Overview
5(1)
Direct Multiple Shooting for DAE
6(3)
Parameterization of the Infinite Optimization Problem
6(1)
Structured Nonlinear Programming Problem
7(1)
Structure of the NLP
8(1)
A Newton-Type Method Solution Framework
8(1)
Initial Value Embedding and Real-Time Iterations
9(3)
Standard Real-Time Iteration Scheme
11(1)
Nominal Stability of the Real-Time Iteration Scheme
12(1)
Real-Time Iteration Variants with Inexact Jacobians
12(6)
Offline Condensing
13(1)
Variant 1: Linear MPC Based on a Reference Trajectory
13(1)
Variant 2: Online Feasibility Improvement
14(1)
Variant 3: Feasibility Improvement for Least-Squares Problems
15(1)
Variant 4: Online Optimality Improvement
16(1)
Multilevel Real-Time Iteration Algorithms
17(1)
Convergence Analysis
18(3)
Stability of the Active Set Near a Solution
19(1)
Convergence for a Given Active Set
20(1)
Conclusions
21(4)
Bibliography
22(3)
A Stabilizing Real-Time Implementation of Nonlinear Model Predictive Control
25(28)
Moritz Diehl
Rolf Findeisen
Frank Allgower
Introduction
25(2)
Discrete-Time Nonlinear Model Predictive Control
27(3)
Online Solution of NMPC: Interconnection of System and Optimizer Dynamics
30(1)
The Real-Time Iteration Scheme
30(3)
Review of Newton-Type Iterations
31(1)
The Real-Time Iteration Algorithm
32(1)
Local Convergence of Newton-Type Optimization
33(2)
Local Convergence of Newton-Type Methods for NMPC
34(1)
Contractivity of the Real-Time Iterations
35(3)
Stability of the Real-Time Iteration Scheme
38(5)
Bounding the Error of Feedback Approximations
38(1)
Combining Error Bound and Contractivity
39(1)
Phase 1: Increase in Objective but Decrease in Stepsize
39(1)
Phase 2: Convergence towards the Origin
40(2)
Nominal Stability of Real-Time Iterations without Shift
42(1)
Numerical Experiments: Distillation Control
43(4)
Optimal Control Problem Formulation
44(1)
Simulation Results and Discussion
45(2)
Summary and Conclusions
47(6)
Bibliography
48(5)
Numerical Feedback Controller Design for PDE Systems Using Model Reduction: Techniques and Case Studies
53(20)
Friedemann Leibfritz
Stefan Volkwein
Introduction
53(1)
Proper Orthogonal Decomposition
54(3)
Numerical Design of SOF Control Laws
57(4)
Numerical Experiments
61(8)
Example (Linear Convection-Diffusion Model)
61(4)
Example (Nonlinear Unstable Heat Equation)
65(1)
Example (Modified Burgers' Equation)
66(3)
Conclusions
69(4)
Bibliography
70(3)
A Least-Squares Finite Element Method for Optimization and Control Problems
73(22)
Pavel B. Bochev
Max D. Gunzburger
Introduction
73(1)
Quadratic Optimization and Control Problems in Hilbert Spaces with Linear Constraints
74(2)
Least-Squares Formulation of the Constraint Equations
76(4)
Methods Based on Constraining by the Least-Squares Functional
80(4)
Discretize-Then-Eliminate
82(1)
Eliminate-Then-Discretize
83(1)
Example: Optimization Problems for the Stokes System
84(8)
Precise Statement of Optimization Problems
84(3)
Least-Squares Formulation of the Constraint Equations
87(1)
Discrete Systems for the Stokes Control Problem
88(2)
Some Practical Issues Arising in Implementations
90(2)
Conclusions
92(3)
Bibliography
92(3)
II. Fast PDE-Constrained Optimization Solvers
95(102)
Space-Time Multigrid Methods for Solving Unsteady Optimal Control Problems
97(18)
Alfio Borzi
Introduction
97(1)
Reaction-Diffusion Optimal Control Problems and Their Approximation
98(2)
Finite Difference Discretization
99(1)
The FAS Multigrid Framework
100(6)
Space-Time Smoothing Schemes
102(3)
Receding Horizon Approach
105(1)
Fourier Smoothing Analysis
106(1)
Numerical Experiments
107(3)
An Application in Physiology
110(5)
Bibliography
111(4)
A Time-Parallel Implicit Methodology for the Near-Real-Time Solution of Systems of Linear Oscillators
115(30)
Julien Cortial
Charbel Farhat
Introduction
115(3)
Two Frameworks for Time-Parallel Algorithms
118(4)
Notation and Definitions
118(2)
The Parareal Framework
120(1)
The PITA Framework
120(1)
Equivalence for Linear Problems
121(1)
Unstable Behavior for Linear Oscillators
122(3)
A New Time-Parallel Framework for Second-Order Hyperbolic Problems
125(4)
Propagation of the Jumps on Both Time Grids
125(1)
Construction of the Subspace Sk
125(2)
Construction of the Projector Pk
127(1)
Computational Remarks
127(1)
PITA for Systems of Linear Oscillators
128(1)
Mathematical Analysis
129(3)
Preliminaries
129(1)
Error Analysis of the Hybrid Coarse/Fine Propagation
130(1)
Convergence in a Subspace
131(1)
Examples
132(4)
Free Vibration of a Three-Dimensional Space Structure
132(1)
Dynamic Responses of an F-16 Fighter Aircraft
133(3)
Summary and conclusions
136(9)
Appendix A
139(1)
Appendix B
140(2)
Bibliography
142(3)
Generalized SQP Methods with ``Parareal'' Time-Domain Decomposition for Time-Dependent PDE-Constrained Optimization
145(24)
Stefan Ulbrich
Introduction
145(1)
Parareal Time-Domain Decomposition
146(4)
Description of the Parareal Method
147(1)
Convergence Properties of the Parareal Algorithm
148(1)
Interpretation as Preconditioned Iteration
149(1)
Time-Domain Decomposition of the Optimal Control Problem
150(2)
Optimality Conditions
151(1)
Structure of the Adjoint Equation
152(1)
A Generalized SQP Method
152(11)
Basic Assumptions
153(1)
Review of Classical Trust-Region SQP Methods
153(2)
Development of the Generalized SQP Algorithm
155(3)
Convergence Result
158(5)
Application of the Generalized SQP Method with Parareal Solvers
163(3)
An Optimal Control Problem for a Semilinear Parabolic Equation
163(1)
Propagators in the Parareal Scheme
163(1)
Implementation of the Generalized SQP Method
164(1)
Numerical Results
164(2)
Conclusions
166(3)
Bibliography
167(2)
Simultaneous Pseudo-Timestepping for State-Constrained Optimization Problems in Aerodynamics
169(14)
Subhendu B. Hazra
Volker Schulz
Introduction
169(1)
Simultaneous Pseudo-Timestepping for Optimization Problems
170(3)
Back Projection
173(2)
Numerical Results and Discussion
175(3)
Conclusions
178(5)
Bibliography
179(4)
Digital Filter Stepsize Control in DASPK and Its Effect on Control Optimization Performance
183(14)
Kirsten Meeker
Chris Homescu
Linda Petzold
Hana El-Samad
Mustafa Khammash
Gustaf Soderlind
Introduction
183(1)
The DAE Solver DASPK
184(1)
Stepsize Controller
185(2)
Original DASPK Approach
185(1)
Digital Filter Stepsize Controller
186(1)
The Optimization Solver KNITRO
187(1)
Heat Shock Model
188(3)
Pareto Optimality
189(1)
Multiobjective Optimization Formulation for Heat Shock
190(1)
Numerical Efficiency Comparison
191(1)
Conclusion
192(5)
Bibliography
194(3)
III. Reduced-Order Modeling
197(54)
Certified Rapid Solution of Partial Differential Equations for Real-Time Parameter Estimation and Optimization
199(18)
Martin A. Grepl
Ngoc C. Nguyen
Karen Veroy
Anthony T. Patera
Gui R. Liu
Introduction
199(1)
Abstract Statement: Elliptic Linear Equations
200(1)
Reduced-Basis Approximation
200(1)
A Posteriori Error Estimation
201(3)
Assess-Act Example: Helmholtz Elasticity
204(3)
Incompressible Navier-Stokes Equations
207(3)
Parabolic Equations
210(7)
Bibliography
214(3)
Model Reduction for Large-Scale Applications in Computational Fluid Dynamics
217(16)
K. Willcox
Introduction
217(2)
Problem Statement
218(1)
Projection Framework
219(1)
Proper Orthogonal Decomposition
219(4)
Time-Domain POD Basis
219(1)
Frequency-Domain POD Basis
220(1)
POD Reduced-Order Model
221(1)
Balanced POD
221(2)
Fourier Model Reduction
223(4)
Fourier Series of Discrete-Time Systems
223(2)
Fourier Series of Continuous-Time Systems
225(1)
Reduced Model Construction
225(1)
FMR Algorithm
226(1)
Active Flow Control of a Supersonic Diffuser
227(3)
Conclusion
230(3)
Bibliography
231(2)
Suboptimal Feedback Control of Flow Separation by POD Model Reduction
233(18)
Karl Kunisch
Lei Xie
Introduction
233(1)
Distributed Volume Control Problem
234(2)
POD-Based Reduced-Order Model
236(4)
Proper Orthogonal Decomposition
236(2)
Reduced-Order Model
238(1)
Construction of Shape Functions
239(1)
Feedback Design for the Reduced Model
240(3)
Discretization of DPE
240(1)
Numerical Methods
241(1)
Retrieving Optimal Control
242(1)
Numerical Results from Volume Control
243(3)
Feedback Design for Boundary Control
246(5)
Bibliography
248(3)
IV. Applications
251(56)
A Combined Shape-Newton and Topology Optimization Technique in Real-Time Image Segmentation
253(24)
Michael Hintermuller
Introduction and Motivation
253(3)
Model and Its Topological Sensitivity
256(5)
A Simple Model
256(1)
Topological Derivative
257(2)
Phase-I Algorithm for Topology Optimization
259(2)
Shape Sensitivity
261(4)
Shape Gradient and Shape Hessian
261(1)
Newton-Type Flow and Descent Properties
262(2)
Phase-II Algorithm for Shape Optimization
264(1)
Numerical Realization and Results
265(7)
Numerical Realization
265(2)
Numerical Results
267(5)
Conclusion
272(5)
Bibliography
274(3)
COFIR: Coarse and Fine Image Registration
277(12)
Jan Modersitzki
Eldad Haber
Introduction
277(2)
The Mathematical Setting
279(1)
Discretization
280(1)
Optimization Scheme
280(3)
Optimizing the Coarse Part
281(1)
Optimizing the Fine Part
281(1)
The Multilevel Approach
282(1)
Numerical Example
283(2)
Conclusions and Further Discussion
285(4)
Bibliography
286(3)
Real-Time, Large-Scale Optimization of Water Network Systems Using a Subdomain Approach
289(18)
Carl D. Laird
Lorenz T. Biegler
Bart G. van Bloemen Waanders
Introduction
289(2)
Contamination Source Determination
290(1)
Outline of the
Chapter
291(1)
Dynamic Optimization Formulation
291(7)
Origin Tracking Algorithm
294(3)
Network Subdomain Approach
297(1)
Numerical Results
298(6)
Results: Fixed Discretization, Variable Problem Size
300(3)
Results: Fixed Problem Size, Variable Discretization
303(1)
Conclusions and Future Work
304(3)
Bibliography
305(2)
Index 307


Lorenz T. Biegler is the Bayer Professor of Chemical Engineering at Carnegie Mellon University. Omar Ghattas is the John A. and Katherine G. Jackson Chair in Computational Geosciences at the University of Texas at Austin. Matthias Heinkenschloss is Professor of Computational and Applied Mathematics at Rice University. David E. Keyes is the Fu Foundation Professor of Applied Mathematics at Columbia University. Bart van Bloemen Waanders is Principal Member of the Technical Staff at Sandia National Laboratories.