Actions for Using automatic differentiation for second-order matrix-free methods in PDE-constrained optimization [electronic resource].

Email RIS file
Bookmark
Report an Issue

Using automatic differentiation for second-order matrix-free methods in PDE-constrained optimization [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 2000.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
vp : digital, PDF file
Additional Creators
Argonne National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
Access Online

Availability

I Want It
I Want It

Finding items...

Restrictions on Access
Free-to-read Unrestricted online access
Summary
Classical methods of constrained optimization are often based on the assumptions that projection onto the constraint manifold is routine but accessing second-derivative information is not. Both assumptions need revision for the application of optimization to systems constrained by partial differential equations, in the contemporary limit of millions of state variables and in the parallel setting. Large-scale PDE solvers are complex pieces of software that exploit detailed knowledge of architecture and application and cannot easily be modified to fit the interface requirements of a blackbox optimizer. Furthermore, in view of the expense of PDE analyses, optimization methods not using second derivatives may require too many iterations to be practical. For general problems, automatic differentiation is likely to be the most convenient means of exploiting second derivatives. We delineate a role for automatic differentiation in matrix-free optimization formulations involving Newton's method, in which little more storage is required than that for the analysis code alone.
Report Numbers
E 1.99:anl/mcs/cp-103457
anl/mcs/cp-103457
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
11/20/2000.
"anl/mcs/cp-103457"
3rd International Conference/Workshop on Automatic Differentiation: From Simulation to Optimization, Nice (FR), 06/19/2000--06/23/2000.
McInnes, L. C.; Hovland, P. D.; Keyes, D. E.; Samyono, W.
Funding Information
W-31109-ENG-38
View MARC record | catkey: 14347320