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Lagrangian Duality in Convex Conic Programming with Simple Proofs

Author

Listed:
  • Maria Trnovska

    (Comenius University in Bratislava)

  • Jakub Hrdina

    (Comenius University in Bratislava)

Abstract

In this paper, we study Lagrangian duality aspects in convex conic programming over general convex cones. It is known that the duality in convex optimization is linked with specific theorems of alternatives. We formulate and prove the strong alternative theorems to the strict feasibility and analyze the relation between the boundedness of the optimal solution sets and the existence of the relative interior points in the feasible set. We also provide sufficient conditions under which the duality gap is zero and the optimal solution sets are unbounded. As a consequence, we obtain several new sufficient conditions that guarantee the strong duality between primal and dual convex conic programs. Our proofs are based only on fundamental convex analysis and linear algebra results.

Suggested Citation

  • Maria Trnovska & Jakub Hrdina, 2023. "Lagrangian Duality in Convex Conic Programming with Simple Proofs," SN Operations Research Forum, Springer, vol. 4(4), pages 1-20, December.
    • Handle: RePEc:spr:snopef:v:4:y:2023:i:4:d:10.1007_s43069-023-00279-4
    DOI: 10.1007/s43069-023-00279-4
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    References listed on IDEAS

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    1. Gábor Pataki, 2007. "On the Closedness of the Linear Image of a Closed Convex Cone," Mathematics of Operations Research, INFORMS, vol. 32(2), pages 395-412, May.
    2. Levent Tunçel & Henry Wolkowicz, 2012. "Strong duality and minimal representations for cone optimization," Computational Optimization and Applications, Springer, vol. 53(2), pages 619-648, October.
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