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Proximal Point Algorithms for Vector DC Programming with Applications to Probabilistic Lot Sizing with Service Levels

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  • Ying Ji
  • Shaojian Qu

Abstract

We present a new algorithm for solving vector DC programming, where the vector function is a function of the difference of C -convex functions. Because of the nonconvexity of the objective function, it is difficult to solve this class of problems. We propose several proximal point algorithms to address this class of problems, which make use of the special structure of the problems (i.e., the DC structure). The well-posedness and the global convergence of the proposed algorithms are developed. The efficiency of the proposed algorithm is shown by an application to a multicriteria model stemming from lot sizing problems.

Suggested Citation

  • Ying Ji & Shaojian Qu, 2017. "Proximal Point Algorithms for Vector DC Programming with Applications to Probabilistic Lot Sizing with Service Levels," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-8, October.
    • Handle: RePEc:hin:jnddns:5675183
    DOI: 10.1155/2017/5675183
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