Symplectic Pontryagin approximations for optimal design

Jesper Carlsson; Mattias Sandberg; Anders Szepessy

ESAIM: Mathematical Modelling and Numerical Analysis (2008)

  • Volume: 43, Issue: 1, page 3-32
  • ISSN: 0764-583X

Abstract

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The powerful Hamilton-Jacobi theory is used for constructing regularizations and error estimates for optimal design problems. The constructed Pontryagin method is a simple and general method for optimal design and reconstruction: the first, analytical, step is to regularize the Hamiltonian; next the solution to its stationary Hamiltonian system, a nonlinear partial differential equation, is computed with the Newton method. The method is efficient for designs where the Hamiltonian function can be explicitly formulated and when the Jacobian is sparse, but becomes impractical otherwise (e.g. for non local control constraints). An error estimate for the difference between exact and approximate objective functions is derived, depending only on the difference of the Hamiltonian and its finite dimensional regularization along the solution path and its L2 projection, i.e. not on the difference of the exact and approximate solutions to the Hamiltonian systems.

How to cite

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Carlsson, Jesper, Sandberg, Mattias, and Szepessy, Anders. "Symplectic Pontryagin approximations for optimal design." ESAIM: Mathematical Modelling and Numerical Analysis 43.1 (2008): 3-32. <http://eudml.org/doc/194446>.

@article{Carlsson2008,
abstract = { The powerful Hamilton-Jacobi theory is used for constructing regularizations and error estimates for optimal design problems. The constructed Pontryagin method is a simple and general method for optimal design and reconstruction: the first, analytical, step is to regularize the Hamiltonian; next the solution to its stationary Hamiltonian system, a nonlinear partial differential equation, is computed with the Newton method. The method is efficient for designs where the Hamiltonian function can be explicitly formulated and when the Jacobian is sparse, but becomes impractical otherwise (e.g. for non local control constraints). An error estimate for the difference between exact and approximate objective functions is derived, depending only on the difference of the Hamiltonian and its finite dimensional regularization along the solution path and its L2 projection, i.e. not on the difference of the exact and approximate solutions to the Hamiltonian systems. },
author = {Carlsson, Jesper, Sandberg, Mattias, Szepessy, Anders},
journal = {ESAIM: Mathematical Modelling and Numerical Analysis},
keywords = {Topology optimization; inverse problems; Hamilton-Jacobi; regularization; error estimates; impedance tomography; convexification; homogenization.; topology optimization; regularization; homogenization; optimal design problems; Pontryagin method; Newton method},
language = {eng},
month = {10},
number = {1},
pages = {3-32},
publisher = {EDP Sciences},
title = {Symplectic Pontryagin approximations for optimal design},
url = {http://eudml.org/doc/194446},
volume = {43},
year = {2008},
}

TY - JOUR
AU - Carlsson, Jesper
AU - Sandberg, Mattias
AU - Szepessy, Anders
TI - Symplectic Pontryagin approximations for optimal design
JO - ESAIM: Mathematical Modelling and Numerical Analysis
DA - 2008/10//
PB - EDP Sciences
VL - 43
IS - 1
SP - 3
EP - 32
AB - The powerful Hamilton-Jacobi theory is used for constructing regularizations and error estimates for optimal design problems. The constructed Pontryagin method is a simple and general method for optimal design and reconstruction: the first, analytical, step is to regularize the Hamiltonian; next the solution to its stationary Hamiltonian system, a nonlinear partial differential equation, is computed with the Newton method. The method is efficient for designs where the Hamiltonian function can be explicitly formulated and when the Jacobian is sparse, but becomes impractical otherwise (e.g. for non local control constraints). An error estimate for the difference between exact and approximate objective functions is derived, depending only on the difference of the Hamiltonian and its finite dimensional regularization along the solution path and its L2 projection, i.e. not on the difference of the exact and approximate solutions to the Hamiltonian systems.
LA - eng
KW - Topology optimization; inverse problems; Hamilton-Jacobi; regularization; error estimates; impedance tomography; convexification; homogenization.; topology optimization; regularization; homogenization; optimal design problems; Pontryagin method; Newton method
UR - http://eudml.org/doc/194446
ER -

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