Abstract
The development of optimization technology has long been a popular research subject. However, with the expansion of the amount of data, the optimization algorithm is becoming more and more important for efficiently loading and solving large parameters. In order to deal with this tendency, this research aims to tackle linear programs with linear complementarity constraints problems (LPCC) with large scale side constraints, and developed an algorithm which combined sequential quadratic programming (SQP) with Bender’s generation cut to obtain the local optimal solutions of LPCC. Eventually, we parallel the algorithm mentioned above by message passing interface (MPI) framework in multiple clustering systems and evaluate the runtime of our algorithm against the prevailing commercial optimization software. From the numerical results, we consider this algorithm, no matter parallel or not, has a significant performance on solving LPCC problems with massive side constraints.