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An efficient DC programming approach for portfolio decision with higher moments

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Abstract

Portfolio selection with higher moments is a NP-hard nonconvex polynomial optimization problem. In this paper, we propose an efficient local optimization approach based on DC (Difference of Convex functions) programming—called DCA (DC Algorithm)—that consists of solving the nonconvex program by a sequence of convex ones. DCA will construct, in each iteration, a suitable convex quadratic subproblem which can be easily solved by explicit method, due to the proposed special DC decomposition. Computational results show that DCA almost always converges to global optimal solutions while comparing with the global optimization methods (Gloptipoly, Branch-and-Bound) and it outperforms several standard local optimization algorithms.

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Authors and Affiliations

  1. Laboratory of Mathematics, National Institute for Applied Sciences, BP 08, Avenue de l’Université, 76801, Rouen, France

    Tao Pham Dinh & Yi-Shuai Niu

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  1. Tao Pham Dinh
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  2. Yi-Shuai Niu
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Correspondence to Tao Pham Dinh.

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Pham Dinh, T., Niu, YS. An efficient DC programming approach for portfolio decision with higher moments. Comput Optim Appl 50, 525–554 (2011). https://doi.org/10.1007/s10589-010-9383-x

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