Multiobjective DC programs with infinite convex constraints

Shaojian Qu (), Mark Goh, Soon-Yi Wu and Robert Souza

Journal of Global Optimization, 2014, vol. 59, issue 1, 58 pages

Abstract: New results are established for multiobjective DC programs with infinite convex constraints (MOPIC) that are defined on Banach spaces (finite or infinite dimensional) with objectives given as the difference of convex functions. This class of problems can also be called multiobjective DC semi-infinite and infinite programs, where decision variables run over finite-dimensional and infinite-dimensional spaces, respectively. Such problems have not been studied as yet. Necessary and sufficient optimality conditions for the weak Pareto efficiency are introduced. Further, we seek a connection between multiobjective linear infinite programs and MOPIC. Both Wolfe and Mond-Weir dual problems are presented, and corresponding weak, strong, and strict converse duality theorems are derived for these two problems respectively. We also extend above results to multiobjective fractional DC programs with infinite convex constraints. The results obtained are new in both semi-infinite and infinite frameworks. Copyright Springer Science+Business Media New York 2014

Keywords: Multiobjective DC programs with infinite convex constraints; Optimality; Duality; Saddle point; (weak) Pareto efficiency (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10898-013-0091-9

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