Invited Session Wed.2.H 2053

Wednesday, 13:15 - 14:45 h, Room: H 2053

Cluster 9: Global optimization [...]

Global optimization methods and applications

 

Chair: Sergiy Butenko

 

 

Wednesday, 13:15 - 13:40 h, Room: H 2053, Talk 1

Panos M. Pardalos
Global optimality conditions in non-convex optimization

Coauthor: Pando G. Georgiev

 

Abstract:
In this talk we are going to present recent results regarding global optimality
conditions for general non-convex optimization problems. First we are
going to discuss complexity issues regarding the existence of points satisfying
optimality conditions and the connection to complementarity problems. In
addition, we are going to discuss surprising connections between optimality
conditions and continuous formulations of discrete optimization problems.
In the second part of the talk we are going to discuss our recent result
regarding optimality conditions of locally Lipschitz functions.

 

 

Wednesday, 13:45 - 14:10 h, Room: H 2053, Talk 2

Erick Moreno-Centeno
Solving combinatorial optimization problems as implicit hitting set problems

Coauthor: Richard M. Karp

 

Abstract:
The hitting set problem is: given a set U and a family S of subsets of U, find a minimum-cardinality set that intersects each set in S. In the implicit hitting set problem, S is given via an oracle which verifies that a given set is a hitting set or returns a not-intersected set from S. Many NP-hard problems can be solved as implicit hitting set problems. We solve the implicit hitting set problem by combining efficient heuristics and exact methods. We present computational results for the minimum-feedback-vertex-set and the multiple-genome alignment problems.
%This is joint work with Professor Richard M. Karp.

 

 

Wednesday, 14:15 - 14:40 h, Room: H 2053, Talk 3

Austin Buchanan
Maximum clique problem on very large scale sparse networks

Coauthors: Sergiy Butenko, Anurag Verma

 

Abstract:
We define a new clique relaxation called a k-community, and explore scale reduction techniques based on it to obtain the maximum clique on very large-scale real life networks. Analytically, the technique has been shown to be very effective on power-law random graphs. Experimental results on real life graph instances (collaboration networks, P2P networks, social networks, etc.) show our procedure to be much more effective than a regular k-core peeling approach.

 

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