Global Optimality Conditions for Classes of Non-convex Multi-objective Quadratic Optimization Problems

Part of the Springer Optimization and Its Applications book series (SOIA, volume 47)


We present necessary and sufficient conditions for identifying global weak minimizers of non-convex multi-objective quadratic optimization problems. We derive these results by exploiting the hidden convexity of the joint range of (non-convex) quadratic functions. We also present numerical examples to illustrate our results.


Quadratic Constraint Joint Range Quadratic Optimization Problem Global Optimality Condition Weak Minimizer 
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The authors are grateful to the referees and the guest editor for their helpful comments and valuable suggestions contributed to the final preparation of the chapter. The second author was supported by the Korea Science and Engineering Foundation (KOSEF) NRL Program grant funded by the Korean government (MEST) (No.ROA-2008-000-20010-0).


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Applied MathematicsUniversity of New South WalesSydneyAustralia
  2. 2.Department of Applied MathematicsPukyong National UniversityBusanKorea

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