379Accesses
5Citations
1Altmetric
Abstract
This paper continues our recent effort in applying continuous optimization techniques to study optimalmulticast communication networks modeled as bilevel hierarchical clustering problems. Given a finite number of nodes, we consider two different models of multicast networks by identifying a certain number of nodes as cluster centers, and at the same time, locating a particular node that serves as a total center so as to minimize the total transportation cost throughout the network. The fact that the cluster centers and the total center have to be among the given nodes makes these problems discrete optimization problems. Our approach is to reformulate the discrete problems as continuous ones and to apply Nesterov’s smoothing approximation techniques on the Minkowski gauges that are used as distance measures. This approach enables us to propose two implementable DCA-based algorithms for solving the problems. Numerical results and practical applications are provided to illustrate our approach.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.



Similar content being viewed by others
References
An, L.T.H., Belghiti, M.T., Tao, P.D.: A new efficient algorithm based on DC programming and DCA for clustering. J. Glob. Optim.27, 503–608 (2007)
An, L.T.H., Minh, L.H., Tao, P.D.: New and efficient DCA based algorithms for minimum sum-of-squares clustering. Pattern Recognit.47, 388–401 (2014)
An, L.T.H., Minh, L.H.: Optimization based DC programming and DCA for hierarchical clustering. Eur. J. Oper. Res.183, 1067–1085 (2007)
An, L.T.H., Tao, P.D.: Convex analysis approach to D.C. programming: theory, algorithms and applications. Acta Math. Vietnam22, 289–355 (1997)
Bagirov, A.: Derivative-free methods for unconstrained nonsmooth optimization and its numerical analysis. Investig. Oper.19, 75–93 (1999)
Bagirov, A., Jia, L., Ouveysi, I., Rubinov, A.M.: Optimization based clustering algorithms in Multicast group hierarchies. In: Proceedings of the Australian Telecommunications, Networks and Applications Conference (ATNAC), Melbourne Australia (published on CD, ISNB 0-646-42229-4) (2003)
Bagirov, A., Taheri, S., Ugon, J.: Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems. Pattern Recognit.53, 12–24 (2016)
Barbosa, G. V., Villas-Boas, S. B., Xavier, A. E.: Solving the two-level clustering problem by hyperbolic smoothing approach, and design of multicast networks. In: Selected Proceedings, WCTR RIO (2013)
Hiriart-Urruty, J.B., Lemaréchal, C.: Convex Analysis and Minimization Algorithms I, II. Springer, Berlin (1993)
Mordukhovich, B.S., Nam, N.M.: An Easy Path to Convex Analysis and Applications. Morgan & Claypool Publishers, San Rafael, CA (2014)
Nam, N.M., Rector, R.B., Giles, D.: Minimizing differences of convex functions with applications to facility location and clustering. J. Optim. Theory Appl.173, 255–278 (2017)
Nam, N.M., Geremew, W., Reynolds, S., Tran, T.: The Nesterov smoothing technique and minimizing differences of convex functions for hierarchical clustering. Optim. Lett.12, 455–473 (2018)
Nesterov, Y.: Smooth minimization of non-smooth functions. Math. Program.103, 127–152 (2005)
Reinelt, G.: TSPLIB: a traveling salesman problem library. ORSA J. Comput.3, 376–384 (1991)
Rockafellar, R.T.: Convex Analysis. Princeton University Press, Princeton, NJ (1970)
Tao, P.D., An, L.T.H.: A d.c. optimization algorithm for solving the trust-region subproblem. SIAM J. Optim.8, 476–505 (1998)
Acknowledgements
This material is based upon work supported by the AFRL Mathematical Modeling and Optimization Institute. The authors are grateful to both anonymous referees for their helpful comments that allowed us to improve the original presentation.
Author information
Authors and Affiliations
School of General Studies, Stockton University, Galloway, NJ, 08205, USA
W. Geremew
Fariborz Maseeh Department of Mathematics and Statistics, Portland State University, Portland, OR, 97207, USA
N. M. Nam
Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
A. Semenov
Industrial Engineering & Management Systems, University of Central Florida, Orlando, FL, 32816, USA
V. Boginski
Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611, USA
V. Boginski
Munitions Directorate, Air Force Research Laboratory, Building 13, Eglin AFB, FL, 32542, USA
E. Pasiliao
- W. Geremew
You can also search for this author inPubMed Google Scholar
- N. M. Nam
You can also search for this author inPubMed Google Scholar
- A. Semenov
You can also search for this author inPubMed Google Scholar
- V. Boginski
You can also search for this author inPubMed Google Scholar
- E. Pasiliao
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toN. M. Nam.
Additional information
Geremew’s research was partly supported by the AFRL Mathematical Modeling and Optimization Institute. Nam’s research was partly supported by the National Science Foundation under Grant DMS-1716057. Semenov’s research was partly supported by the European Office of Aerospace Research and Development under Grant FA9550-17-1-0030.
Rights and permissions
About this article
Cite this article
Geremew, W., Nam, N.M., Semenov, A.et al. A DC programming approach for solving multicast network design problems via the Nesterov smoothing technique.J Glob Optim72, 705–729 (2018). https://doi.org/10.1007/s10898-018-0671-9
Received:
Accepted:
Published:
Issue Date:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative