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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)

Abstract:

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.

 

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