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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>
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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>
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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
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