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

In: Variational Analysis and Generalized Differentiation in Optimization and Control

Author

Listed:
  • V. Jeyakumar

    (University of New South Wales)

  • G. M. Lee

    (Pukyong National University)

  • G. Li

    (University of New South Wales)

Abstract

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.

Suggested Citation

  • V. Jeyakumar & G. M. Lee & G. Li, 2010. "Global Optimality Conditions for Classes of Non-convex Multi-objective Quadratic Optimization Problems," Springer Optimization and Its Applications, in: Regina S. Burachik & Jen-Chih Yao (ed.), Variational Analysis and Generalized Differentiation in Optimization and Control, pages 177-186, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-0437-9_9
    DOI: 10.1007/978-1-4419-0437-9_9
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    Cited by:

    1. M. Ruiz Galán, 2017. "A theorem of the alternative with an arbitrary number of inequalities and quadratic programming," Journal of Global Optimization, Springer, vol. 69(2), pages 427-442, October.
    2. P. Montiel López & M. Ruiz Galán, 2016. "Nonlinear Programming via König’s Maximum Theorem," Journal of Optimization Theory and Applications, Springer, vol. 170(3), pages 838-852, September.

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