Large-Scale PDE-Constrained Optimization

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.

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

• 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

Policies and ethics