Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Parallel Pareto Optimization for Subset Selection / 1939
Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou

Subset selection that selects a few variables from a large set is a fundamental problem in many areas. The recently emerged Pareto Optimization for Subset Selection (POSS) method is a powerful approximation solver for this problem. However, POSS is not readily parallelizable, restricting its large-scale applications on modern computing architectures. In this paper, we propose PPOSS, a parallel version of POSS. Our theoretical analysis shows that PPOSS has good properties for parallelization while preserving the approximation quality: when the number of processors is limited (less than the total number of variables), the running time of PPOSS can be reduced almost linearly with respect to the number of processors; with increasing number of processors, the running time can be further reduced, eventually to a constant. Empirical studies verify the effectiveness of PPOSS, and moreover suggest that the asynchronous implementation is more efficient with little quality loss.