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A Branch-and-Bound Algorithm Embedded with DCA for DC Programming

Author

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  • Meihua Wang
  • Fengmin Xu
  • Chengxian Xu

Abstract

The special importance of Difference of Convex (DC) functions programming has been recognized in recent studies on nonconvex optimization problems. In this work, a class of DC programming derived from the portfolio selection problems is studied. The most popular method applied to solve the problem is the Branch-and-Bound (B&B) algorithm. However, “the curse of dimensionality†will affect the performance of the B&B algorithm. DC Algorithm (DCA) is an efficient method to get a local optimal solution. It has been applied to many practical problems, especially for large-scale problems. A B&B-DCA algorithm is proposed by embedding DCA into the B&B algorithms, the new algorithm improves the computational performance and obtains a global optimal solution. Computational results show that the proposed B&B-DCA algorithm has the superiority of the branch number and computational time than general B&B. The nice features of DCA (inexpensiveness, reliability, robustness, globality of computed solutions, etc.) provide crucial support to the combined B&B-DCA for accelerating the convergence of B&B.

Suggested Citation

  • Meihua Wang & Fengmin Xu & Chengxian Xu, 2012. "A Branch-and-Bound Algorithm Embedded with DCA for DC Programming," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-16, July.
    • Handle: RePEc:hin:jnlmpe:364607
    DOI: 10.1155/2012/364607
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