<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>7703</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>444</pageFirst>
    <pageLast>458</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>81</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-03-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Kaskade 7 - a Flexible Finite Element Toolbox</title>
    <abstract language="eng">Kaskade 7 is a finite element toolbox for the solution of stationary or transient systems of partial differential equations, aimed at supporting application-oriented research in numerical analysis and scientific computing. The library is written in C++ and is based on the \textsc{Dune} interface. The code is independent of spatial dimension and works with different grid managers. An important feature is the mix-and-match approach to discretizing systems of PDEs with different ansatz and test spaces for all variables. &#13;
&#13;
We describe the mathematical concepts behind the library as well as its structure, illustrating its use at several examples on the way.</abstract>
    <parentTitle language="eng">Computers and Mathematics with Applications</parentTitle>
    <identifier type="doi">10.1016/j.camwa.2020.02.011</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2020-02-11</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-74616</enrichment>
    <submitter>Martin Weiser</submitter>
    <author>Sebastian Götschel</author>
    <author>Anton Schiela</author>
    <author>Martin Weiser</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="weiser">Weiser, Martin</collection>
    <collection role="projects" number="DFG-RTThermo">DFG-RTThermo</collection>
    <collection role="projects" number="ECMath-CH9">ECMath-CH9</collection>
    <collection role="projects" number="ZIB-Kaskade7">ZIB-Kaskade7</collection>
    <collection role="projects" number="HighPerMeshes">HighPerMeshes</collection>
    <collection role="projects" number="ECMath-CH20">ECMath-CH20</collection>
    <collection role="institutes" number="MfLMS">Mathematics for Life and Materials Science</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
  </doc>
  <doc>
    <id>8076</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>A4095</pageFirst>
    <pageLast>A4120</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>43</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A primal dual projection algorithm for efficient constraint preconditioning</title>
    <abstract language="eng">We consider a linear iterative solver for large scale linearly constrained quadratic minimization problems that arise, for example, in optimization with PDEs. By a primal-dual projection (PDP) iteration, which can be interpreted and analysed as a gradient method on a quotient space, the given problem can be solved by computing sulutions for a sequence of constrained surrogate problems, projections onto the feasible subspaces, and Lagrange multiplier updates. As a major application we consider a class of optimization problems with PDEs, where PDP can be applied together with a projected cg method using a block triangular constraint preconditioner. Numerical experiments show reliable and competitive performance for an optimal control problem in elasticity.</abstract>
    <parentTitle language="eng">SIAM Journal on Scientific Computing</parentTitle>
    <identifier type="doi">10.1137/20M1380739</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2021-08-18</enrichment>
    <author>Anton Schiela</author>
    <submitter>Martin Weiser</submitter>
    <author>Matthias Stöcklein</author>
    <author>Martin Weiser</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="persons" number="weiser">Weiser, Martin</collection>
    <collection role="projects" number="ZIB-Kaskade7">ZIB-Kaskade7</collection>
    <collection role="projects" number="2020-SPP1962">2020-SPP1962</collection>
    <collection role="institutes" number="MfLMS">Mathematics for Life and Materials Science</collection>
    <collection role="institutes" number="MSoCP">Modeling and Simulation of Complex Processes</collection>
  </doc>
</export-example>
