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DC programming approaches for discrete portfolio optimization under concave transaction costs

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Abstract

The paper investigates DC programming and DCA for both modeling discrete portfolio optimization under concave transaction costs as DC programs, and their solution. DC reformulations are established by using penalty techniques in DC programming. A suitable global optimization branch and bound technique is also developed where a DC relaxation technique is used for lower bounding. Numerical simulations are reported that show the efficiency of DCA and the globality of its computed solutions, compared to standard algorithms for nonconvex nonlinear integer programs.

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Authors and Affiliations

  1. LMI, National Institute for Applied Sciences-Rouen, University of Normandie, 76801, Saint-Étienne-du-Rouvray Cedex, France

    Tao Pham Dinh, Viet Nga Pham & Yi-Shuai Niu

  2. Laboratory of Theoretical and Applied Computer Science, University of Lorraine, Ile du Saulcy, 57045, Metz, France

    Hoai An Le Thi

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  1. Tao Pham Dinh
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  2. Hoai An Le Thi
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  3. Viet Nga Pham
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  4. Yi-Shuai Niu
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Correspondence to Hoai An Le Thi.

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Pham Dinh, T., Le Thi, H.A., Pham, V.N. et al. DC programming approaches for discrete portfolio optimization under concave transaction costs. Optim Lett 10, 261–282 (2016). https://doi.org/10.1007/s11590-015-0931-2

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