Large-Scale PDE-Constrained Optimization

  • Lorenz T. Biegler
  • Matthias Heinkenschloss
  • Omar Ghattas
  • Bart van Bloemen Waanders
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 30)

Table of contents

  1. Front Matter
    Pages I-VI
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. 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
    2. D. P. Young, W. P. Huffman, R. G. Melvin, C. L. Hilmes, F. T. Johnson
      Pages 17-43
    3. 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
    2. Natalia M. Alexandrov, Robert Michael Lewis
      Pages 63-79
    3. Steven E. Cox, Raphael T. Haftka
      Pages 80-92
  5. Sensitivities for PDE-based Optimization

    1. Front Matter
      Pages 115-115
    2. Paul Hovland, Steven Lee, Lois McInnes, Boyana Norris, Barry Smith
      Pages 133-147
    3. Andreas Griewank, Christèle Faure
      Pages 148-164
  6. NLP Algorithms and Inequality Constraints

    1. Front Matter
      Pages 165-165
    2. José Luis Morales, Jorge Nocedal, Richard A. Waltz, Guanghui Liu, Jean-Pierre Goux
      Pages 167-183
    3. John T. Betts, Samuel K. Eldersveld, Paul D. Frank, John G. Lewis
      Pages 184-198
    4. Lorenz T. Biegler, Andreas Wächter
      Pages 199-217
    5. Randolph E. Bank, Philip E. Gill, Roummel F. Marcia
      Pages 218-235
    6. Anthony J. Kearsley, Jon W. Tolle, Paul T. Boggs
      Pages 236-249

About these proceedings

Introduction

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.

Keywords

PDE-constrained optimization inverse problems large-scale optimization optimal control optimal design partial differential equation scientific computing

Editors and affiliations

  • Lorenz T. Biegler
    • 1
  • Matthias Heinkenschloss
    • 2
  • Omar Ghattas
    • 3
  • Bart van Bloemen Waanders
    • 4
  1. 1.Department of Chemical EngineeringCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Computational and Applied MathematicsRice UniversityHoustonUSA
  3. 3.Department of Biomedical Engineering and Civil & Environmental EngineeringCarnegie Mellon UniversityPittsburghUSA
  4. 4.Optimization & Uncertainty Estimation Dept.Sandia National Laboratories — MS 0847AlbuquerqueUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-55508-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-05045-2
  • Online ISBN 978-3-642-55508-4
  • Series Print ISSN 1439-7358
  • About this book