Username   Password       Forgot your password?  Forgot your username? 


Auto-Tuning for Solving Multi-Conditional MAD Model

Volume 14, Number 5, May 2018, pp. 1030-1039
DOI: 10.23940/ijpe.18.05.p22.10301039

Feng Yaoa, Yi Liua, Huifen Chena, Chen Lib,c, Zhonghua Lub, Jinggang Wanga, Zhiheng Lia, and Ningming Nieb

aState Grid HeNan Electric Power Company, Zhengzhou, 450052, China
bComputer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China
cUniversity of Chinese Academy of Sciences, Beijing, 100049, China

(Submitted on February 2, 2018; Revised on March 8, 2018; Accepted on April 17, 2018)


As an important branch of Integer Programming (IP), Mixed Integer Nonlinear Programming (MINLP) has been applied in many fields. As a typical MINLP model, solving the multi-conditional MAD model is a NP-hard problem. In order to solve the model efficiently and rapidly, an auto-tuning of the branch-cut algorithm, which is the solving algorithm of the multi-conditional MAD model, is performed by using the CPLEX solver deployed on the Era supercomputer. The experimental results show that the parallel branch-cut algorithm after auto-tuning can improve the computation speed significantly and can obtain comparable results with the algorithm before auto-tuning, and the parallel efficiencies are better and preponderate over 60% when the number of threads is 2 or 4.


References: 13

      1. C. S. Adjiman, I. P. Androulakis, and C. A. Floudas, “Global Optimization of MINLP Problems in Process Synthesis and Design,” Computers & Chemical Engineering, vol. 21, no. 10, pp. 445-450, 1997
      2. R. J. Allgor and P. I. Barton, “Mixed-Integer Dynamic Optimization,” Computers & Chemical Engineering, vol. 21, no. 21, pp. 451-456, 1997
      3. P. Bonami, L. T. Biegler, A. R. Conn, et al. “An Algorithmic Framework for Convex Mixed Integer Nonlinear Programs,” Discrete Optimization, vol. 5, no. 2, pp. 186-204, 2008
      4. R. J. Dakin, “A Tree-Search Algorithm for Mixed Integer Programming Problems,” Computer Journal, vol. 8, no. 3, pp. 250-255, 1965
      5. M. A. Duran and I. E. Grossmann, “An Outer-Approximation Algorithm for a Class of Mixed-Integer Nonlinear Programs,” Mathematical Programming, vol. 36, no. 3, pp. 307-339, 1986
      6. D. C. Faria and M. J. Bagajewicz, “A New Approach for Global Optimization of a Class of MINLP Problems with Applications to Water Management and Pooling Problems,” Aiche Journal, vol. 58, no. 8, pp. 2320-2335, 2012
      7. M. R. Garey and D. S. Johnson, “Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman, 1983
      8. A. M. Geoffrion, “Generalized Benders Decomposition,” Journal of Optimization Theory & Applications, vol. 10, no. 4, pp. 237-260, 1972
      9. D. Han, J. Jian, and L. Yang, “Outer Approximation and Outer-Inner Approximation Approaches for Unit Commitment Problem,” IEEE Transactions on Power Systems, vol. 29, no. 2, pp. 505-513, 2014
      10. C. Li, Z. H. Lu, J. L. Hu, Y. H. Hu, and J. Wang, “Portfolio Optimization Problem Solving Under Concave Transaction Costs and Cardinality Constraints,” in Proceedings of CCF HPC China 2016, pp. 739-742, Xi’an, China, October 2016 (In Chinese)
      11. M. L. Luyben and C. A. Floudas, “Analysis the Interaction of Design and Control, Part 2: Reactor-Separator-Recycle Systems,” Computers & Chemical Engineering, vol. 18, no. 10, pp. 971-993, 1994
      12. Z. H. Shen, Y. Dong, and X. X. Wang, “Logistics Base Location Optimization Model Based on Mixed Integer Non-linear Program,” Logistics Sci-Tech, vol. 36, no. 7, pp. 89-93, 2013 (In Chinese)
      13. T. Westerlund and F. Pettersson, “An Extended Cutting Plane Method for Solving Convex MINLP Problems,” Computers & Chemical Engineering, vol. 19, no. 1, pp. 131-136, 1995


          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

          This site uses encryption for transmitting your passwords.