Friday, 16:15 - 16:40 h, Room: H 2053


Syuuji Yamada
Global optimization methods utilizing partial separating hyperplanes for a canonical dc programming problem

Coauthors: Tamaki Tanaka, Tetsuzo Tanino


In this talk, we consider a canonical dc programming problem (CDC) to minimize a linear function over the difference between a compact convex set and an open bounded convex set. It is known that many global optimization problems can be transformed into (CDC). Hence, for (CDC), many approximation algorithms based on outer approximation methods and branch-and-bound procedures have been proposed. However, since the volume of data necessary for executing such algorithms increases in proportion to the number of iterations, such algorithms are not effective for large scale problems. Hence, to calculate an approximate solution of a large scale (CDC), we propose new iterative solution methods. To avoid the growth of data storage, the proposed methods find an approximate solution of (CDC) by rotating a partial separating hyperplane around a convex set defining the feasible set at each iteration. Moreover, in order to improve the computational efficiency of the proposed methods, we utilize the polar coordinate system.


Talk 3 of the contributed session Fri.3.H 2053
"Advances in global optimization VI" [...]
Cluster 9
"Global optimization" [...]


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