Time: 10:00 to  11:30 Ngày 20/02/2019

Venue/Location: C2-714, VIASM

Speaker: JAE HYOUNG LEE (Pukyong National University)


In this talk, we focus on finding efficient solutions for a multi-objective opti-mization problem with convex polynomial data. We first prove an existence result of efficient solutions of the proposed multi-objective optimization problem under some mild assump-tion. By employing hybrid method, we consider a (scalar) convex polynomial optimization problem to find efficient solutions of the proposed multi-objective optimization. We do this by establishing two kind representation of non-negativity of convex polynomials over convex semi-algebraic sets, and then we propose two kind finite convergence results of the Lasserre-type hierarchy of semidefinite programming relaxations for the (scalar) convex polynomial optimization problem under suitable assumptions. As a result, we show that finding efficient solutions of the proposed multi-objective optimization can be done via solving hierarchies of semidefinite programming relaxations and checking a flat truncation condition.

(Liguo Jiao) Finance·Fishery·Manufacture Industrial Mathematics Center on Big Data, Pusan National University, Busan 46241, Korea
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(Jae Hyoung Lee) Department of Applied Mathematics, Pukyong National University, Bu-san, 48513, Korea
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(Nithirat Sisarat) Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok 65000, Thailand
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