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UID:news926@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20191130T081127
DTSTART;TZID=Europe/Zurich:20191206T110000
SUMMARY:Seminar in Numerical Analysis: Ira Neitzel (Universität Bonn)
DESCRIPTION:joint work with Dominik Hafemeyer\, Florian Mannel and Boris Ve
 xler\\r\\n We consider a convex  optimal control problem governed by a par
 tial differential equation in  one space dimension which is controlled by 
 a right-hand-side living in  the space of functions with bounded variation
 . These functions tend to favor optimal controls that are piecewise  const
 ant with often finitely many jump poins. We are interested in  deriving fi
 nite element discretization error estimates for the controls  when the sta
 te ist discretized with usual piecewise linear finite elements\, and the c
 ontrols is either variationally  discrete or piecwise constant. Due to the
  structure of the objective  function\, usual techniques for estimating th
 e control error cannot be  applied. Instead\, these have to be derived fro
 m (suboptimal) error estimates for the state\, which can later be improved
 . \\r\\nFor further information about the seminar\, please visit this web
 page [t3://page?uid=current].
X-ALT-DESC:<p>joint work with Dominik Hafemeyer\, Florian Mannel and Boris 
 Vexler</p>\n<p><br /> We consider a convex  optimal control problem govern
 ed by a partial differential equation in  one space dimension which is con
 trolled by a right-hand-side living in  the space of functions with bounde
 d variation. These functions tend to favor optimal controls that are piece
 wise  constant with often finitely many jump poins. We are interested in  
 deriving finite element discretization error estimates for the controls  w
 hen the state ist discretized with usual piecewise linear finite elements\
 , and the controls is either variationally  discrete or piecwise constant.
  Due to the structure of the objective  function\, usual techniques for es
 timating the control error cannot be  applied. Instead\, these have to be 
 derived from (suboptimal) error estimates for the state\, which can later 
 be improved. </p>\n<p>For further information about the seminar\, please v
 isit this&nbsp\;<a href="t3://page?uid=current" title="Opens internal link
  in current window">webpage</a>.</p>
DTEND;TZID=Europe/Zurich:20191206T120000
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