An Improved Algorithm for Parallelizing Sequential Minimal Optimization
C.R. Li, Jun Hai Guo · 2015
In our previous work, a parallelizing sequential minimization optimization was proposed, where the algorithm was executed successfully but its convergence cannot be guaranteed in some cases.In this paper, an improved version is proposed, which can avoid falling into the endless loops.In the proposed method, the multiple violation pairs are selected in each step, and depending on the decrement value of the objective function, a single-pair update or multiple-pair update is determined.Experimental results show that the proposed method is more effective than the previous methods.The parallel algorithm is well executed while the accuracy is maintained and the convergence is completely guaranteed.