【学术会议】Learning Generalized Strong Branching for Set Covering, Set Packing, and 0-1 Knapsack Problems - Mr. Yu Yang
主题: Learning Generalized Strong Branching for Set Covering, Set Packing, and 0-1 Knapsack Problems
报告人: Mr. Yu Yang, Georgia Institute of Technology
时间: 15:30 - 16:30, Friday, December 13, 2019
地点: Room 110, Zhi Xin Building
Set branching, a natural generalization of standard branching, branches on a set of variables, which potentially gives tighter local bounds and consequently yields a smaller search tree. However, selecting a good set to branch on in this case becomes a even more complicated problem than choosing a good single branching variable. Generalized strong branching on sets up to size k (GSB-k) considers sets of size no larger than k and chooses the branching set in a strong branching fashion. It’s prohibitively time-consuming due to the overhead incurred in solving a large number of linear programming (LP) relaxations. We apply machine learning techniques to learn GSB-2 and demonstrate that the learned branching scheme LRN-GSB signiﬁcantly outperforms the default branching scheme (CPLEX-D) employed in the state-of-the-art commercial solver CPLEX on set covering, set packing, and 0-1 knapsack problems. Moreover, LRN-GSB is robust in the sense that it is able to consistently beat CPLEX-D on large (hard) instances if trained only on small (easy) instances.
Yu Yang is a Ph.D. candidate in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech, under the supervision of Martin Savelsbergh and Natashia Boland. He received his B.S. in Applied Mathematics from Peking University in China. His interests lie broadly in discrete and continuous optimization. His current focus is on eﬃciently solving non-convex optimization models both theoretically and practically. He has been working on the following three research streams: 1) eﬃcient branching strategies for classical integer programs (IP’s); 2) randomized accelerated ﬁrst-order methods for non-convex optimization in statistical learning; 3) applications involving large-scale optimization models with the potential to impact the real world.