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<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>When:
Thursday,
April 10, 1:00pm<b><span style='font-weight:bold'><o:p></o:p></span></b></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Where:
TTI-C Conference Room<o:p></o:p></span></font></p>
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font-family:Arial'><o:p> </o:p></span></font></p>
<p style='margin:0in;margin-bottom:.0001pt'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>Who:
Richard Waltz, <st1:place w:st="on"><st1:PlaceType w:st="on">University</st1:PlaceType>
of <st1:PlaceName w:st="on">Southern California</st1:PlaceName></st1:place><o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>Topic:
New
Active-Set Algorithms for Large-Scale Nonlinear Optimization<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
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<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Active-set algorithms offer a powerful approach for solving
nonlinear optimization problems. These methods have many advantages over the more
recently popular interior-point methods; most notably the ability to converge
quickly (i.e., "warm start") from an advanced initial point. However,
current active-set methods are unable to scale to large problem sizes as
effectively as interior-point methods, and this significantly limits their
applicability. <br>
<br>
This talk will present new techniques for identifying which inequality constraints
are "active" (i.e., hold as equalities) at the solution of nonlinear
optimization problems. These techniques, based on solving linear programming
subproblems, allow the active-set estimate to change by many constraints at
once and overcome the bottlenecks of traditional active-set methods. We also
present advances in penalty methods used to relax constraints in nonlinear
optimization models. These penalty methods are integrated with our new
active-set identification techniques to form a novel active-set algorithm that outperforms
traditional active-set methods on large-scale nonlinear optimization problems.</span></font><font
size=2 face=Arial><span style='font-size:10.0pt;font-family:Arial'><o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Contact:
Nathan Srebro,
TTI-C nati@tti-c.org
834-7493<o:p></o:p></span></font></p>
<p style='margin:0in;margin-bottom:.0001pt'><font size=3 face="Times New Roman"><span
style='font-size:12.0pt'> </span></font><font color=navy face=Arial><span
style='font-family:Arial;color:navy'><o:p></o:p></span></font></p>
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