Invited Session Thu.1.MA 313

Thursday, 10:30 - 12:00 h, Room: MA 313

Cluster 3: Complementarity & variational inequalities [...]

Bilevel programs and MPECs


Chair: Jane Ye



Thursday, 10:30 - 10:55 h, Room: MA 313, Talk 1

Chao Ding
First order optimality conditions for mathematical programs with semidefinite cone complementarity constraints

Coauthors: Defeng Sun, Jane Ye


In this talk we consider a mathematical program with semidefinite cone complementarity constraints (SDCMPCC). Such a problem is a matrix analogue of the mathematical program with (vector) complementarity constraints (MPCC) and includes MPCC as a special case. We derive explicit expressions for the strong-, Mordukhovich- and Clarke- (S-, M- and C-)stationary conditions and give constraint qualifications under which a local solution of SDCMPCC is a S-, M- and C-stationary point.



Thursday, 11:00 - 11:25 h, Room: MA 313, Talk 2

Stephan Dempe
Optimality conditions for bilevel programming problems

Coauthor: Alain B. Zemkoho


Bilevel programming problems are hierarchical optimization problems where the feasible region is (in part) restricted to the graph of the solution set mapping of a second parametric optimization problem. To solve them and to derive optimality conditions for these problems this parametric optimization problem needs to be replaced with its (necessary) optimality conditions. This results in a (one-level) optimization problem. In the talk different approaches to transform the bilevel programming will be suggested, and relations between the original bilevel problem and the one replacing it will be investigated. Necessary optimality conditions being based
on these transformations will be formulated.



Thursday, 11:30 - 11:55 h, Room: MA 313, Talk 3

Jane Ye
On solving bilevel programs with a nonconvex lower level program

Coauthors: Guihua Lin, Mengwei Xu


By using the value function of the lower level program, we reformulate a simple bilevel program where the lower level program is a nonconvex minimization problem with a convex set constraint as a single level optimization problem with a nonsmooth inequality constraint and a convex set constraint. To deal with such a nonsmooth and nonconvex optimization problem, we design a smoothing projected gradient algorithm for a general optimization problem with a nonsmooth inequality constraint and a convex set constraint. We show that, if either the sequence of the penalty parameters is bounded or the extended Mangasarian-Fromovitz constraint qualification holds at the accumulation point of the iteration points, any accumulation point
is a stationary point of the nonsmooth optimization problem. We apply the smoothing projected gradient algorithm to the bilevel program if the calmness condition holds and to an approximate bilevel program otherwise.


  There are three major facts that should be watched out for in all payday loans in the United States. In this section we give only a brief summary recommendation for admission of Levitra. Full information can be found in the instructions for receiving medications with vardenafil.