Active set methods and the semismooth Newton method for convex quadratic programming

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Időpont: 
2017. May 25. 14:15
Helyszín: 
H building, room 306
Kategória: 
Előadás
Szervezés: 
BME-egyetem
Kapcsolattartó: 
Department of differential equations
Lecturers: Philipp Hungerlaender and Franz Rendl  Alpen-Adria Universitaet Klagenfurt
 
Summary:
 
The semismooth Newton method of Kunisch et al for bound constrained convex quadratic programming is extremely efficient, if it converges.Unfortunately, global convergence may fail in general.
 
We first present two variants to make it globally convergent, one uses recursion, the other a type of combinatorial line search. Both variants maintain the positive features of the SN-Method, and there does not seem to be a clear champion among the two.
 
Finally, we address modifications to make the SN-Method applicable to general convex quadratic problems, including linear equality constraints. First computational experiments look very encouraging.