Auto-Tuning for Solving Multi-Conditional MAD Model

doi:10.23940/ijpe.18.05.p22.10301039

Abstract

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.

Submitted on February 2, 2018; Revised on March 8, 2018; Accepted on April 17, 2018
References: 13

Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie. Auto-Tuning for Solving Multi-Conditional MAD Model [J]. Int J Performability Eng, 2018, 14(5): 1030-1039.

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