Publication: Implementation of Partial Separability in a Source-to-Source Transformation AD Tool
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Implementation of Partial Separability in a Source-to-Source Transformation AD Tool

- incollection -
 

Author(s)
Sri Hari Krishna Narayanan , Boyana Norris , Paul Hovland , Assefaw Gebremedhin

Published in
Recent Advances in Algorithmic Differentiation

Editor(s)
Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther

Year
2012

Publisher
Springer

Abstract
A significant number of large optimization problems exhibit structure known as partial separability, for example, least squares problems, where elemental functions are gathered into groups that are then squared. The sparsity of the Jacobian of a partially separable function can be exploited by computing the smaller Jacobians of the elemental functions and then assembling them into the full Jacobian. We implemented partial separability support in ADIC2 by using pragmas to identify partially separable function values, applying source transformations to subdivide the elemental gradient computations, and using the ColPack coloring toolkit to compress the sparse elemental Jacobians. We present experimental results for an elastic-plastic torsion optimization problem from the MINPACK-2 test suite.

Cross-References
Forth2012RAi

AD Theory and Techniques
partial separability

BibTeX
@INCOLLECTION{
         Narayanan2012IoP,
       title = "Implementation of Partial Separability in a Source-to-Source Transformation {AD}
         Tool",
       doi = "10.1007/978-3-642-30023-3_31",
       author = "Sri Hari Krishna Narayanan and Boyana Norris and Paul Hovland and Assefaw
         Gebremedhin",
       abstract = "A significant number of large optimization problems exhibit structure known as
         partial separability, for example, least squares problems, where elemental functions are gathered
         into groups that are then squared. The sparsity of the Jacobian of a partially separable function
         can be exploited by computing the smaller Jacobians of the elemental functions and then assembling
         them into the full Jacobian. We implemented partial separability support in ADIC2 by using pragmas
         to identify partially separable function values, applying source transformations to subdivide the
         elemental gradient computations, and using the ColPack coloring toolkit to compress the sparse
         elemental Jacobians. We present experimental results for an elastic-plastic torsion optimization
         problem from the MINPACK-2 test suite.",
       pages = "343--353",
       crossref = "Forth2012RAi",
       booktitle = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       volume = "87",
       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2012",
       ad_theotech = "partial separability"
}


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