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Large-Scale PDE-Constrained Optimization

  • Conference proceedings
  • © 2003

Overview

Editors:
  1. Lorenz T. Biegler

    1. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, USA

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  2. Matthias Heinkenschloss

    1. Department of Computational and Applied Mathematics, Rice University, Houston, USA

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  3. Omar Ghattas

    1. Department of Biomedical Engineering and Civil & Environmental Engineering, Carnegie Mellon University, Pittsburgh, USA

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    You can also search for this editor in PubMed   Google Scholar

  4. Bart Bloemen Waanders

    1. Optimization & Uncertainty Estimation Dept., Sandia National Laboratories — MS 0847, Albuquerque, USA

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Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 30)

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Table of contents (20 papers)

  1. Front Matter

    Pages I-VI
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Large-Scale PDE-Constrained Optimization: An Introduction

      • Lorenz T. Biegler, Omar Ghattas, Matthias Heinkenschloss, Bart van Bloemen Waanders
      Pages 3-13

  3. Large-Scale CFD Applications

    1. Front Matter

      Pages 15-15
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    2. Nonlinear Elimination in Aerodynamic Analysis and Design Optimization

      • D. P. Young, W. P. Huffman, R. G. Melvin, C. L. Hilmes, F. T. Johnson
      Pages 17-43

    3. Optimization of Large-Scale Reacting Flows using MPSalsa and Sequential Quadratic Programming

      • A. G. Salinger, R. P. Pawlowski, J. N. Shadid, B. van Bloemen Waanders, R. Bartlett, G. C. Itle et al.
      Pages 45-59

  4. Multifidelity Models and Inexactness

    1. Front Matter

      Pages 61-61
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    2. First-Order Approximation and Model Management in Optimization

      • Natalia M. Alexandrov, Robert Michael Lewis
      Pages 63-79

    3. Multifidelity Global Optimization Using DIRECT

      • Steven E. Cox, Raphael T. Haftka
      Pages 80-92

    4. Inexactness Issues in the Lagrange-Newton-Krylov-Schur Method for PDE-constrained Optimization

      • George Biros, Omar Ghattas
      Pages 93-114

  5. Sensitivities for PDE-based Optimization

    1. Front Matter

      Pages 115-115
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    2. Solution Adapted Mesh Refinement and Sensitivity Analysis for Parabolic Partial Differential Equation Systems

      • Shengtai Li, Linda R. Petzold
      Pages 117-132

    3. Challenges and Opportunities in Using Automatic Differentiation with Object-Oriented Toolkits for Scientific Computing

      • Paul Hovland, Steven Lee, Lois McInnes, Boyana Norris, Barry Smith
      Pages 133-147

    4. Piggyback Differentiation and Optimization

      • Andreas Griewank, Christèle Faure
      Pages 148-164

  6. NLP Algorithms and Inequality Constraints

    1. Front Matter

      Pages 165-165
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    2. Assessing the Potential of Interior Methods for Nonlinear Optimization

      • José Luis Morales, Jorge Nocedal, Richard A. Waltz, Guanghui Liu, Jean-Pierre Goux
      Pages 167-183

    3. An Interior-Point Algorithm for Large Scale Optimization

      • John T. Betts, Samuel K. Eldersveld, Paul D. Frank, John G. Lewis
      Pages 184-198

    4. SQP SAND Strategies that Link to Existing Modeling Systems

      • Lorenz T. Biegler, Andreas Wächter
      Pages 199-217

    5. Interior Methods For a Class of Elliptic Variational Inequalities

      • Randolph E. Bank, Philip E. Gill, Roummel F. Marcia
      Pages 218-235

    6. Hierarchical Control of a Linear Diffusion Equation

      • Anthony J. Kearsley, Jon W. Tolle, Paul T. Boggs
      Pages 236-249
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Keywords

About this book

Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. With the maturing of technology for PDE simulation, interest has now increased in PDE-based optimization. The chapters in this volume collectively assess the state of the art in PDE-constrained optimization, identify challenges to optimization presented by modern highly parallel PDE simulation codes, and discuss promising algorithmic and software approaches for addressing them. These contributions represent current research of two strong scientific computing communities, in optimization and PDE simulation. This volume merges perspectives in these two different areas and identifies interesting open questions for further research.

Editors and Affiliations

  • Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, USA

    Lorenz T. Biegler

  • Department of Computational and Applied Mathematics, Rice University, Houston, USA

    Matthias Heinkenschloss

  • Department of Biomedical Engineering and Civil & Environmental Engineering, Carnegie Mellon University, Pittsburgh, USA

    Omar Ghattas

  • Optimization & Uncertainty Estimation Dept., Sandia National Laboratories — MS 0847, Albuquerque, USA

    Bart Bloemen Waanders

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