2 citations · 4 across the 3 of their papers we have counts for
4 papers
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem
Jiongzhi Zheng, Kun He, Jianrong Zhou +2
We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, wh…
Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem
Kun He, Min Zhang, Jianrong Zhou +2
Stochastic gradient descent (SGD) is a powerful method for large-scale optimization problems in the area of machine learning, especially for a finite-sum formulation with numerous…
A Learning based Branch and Bound for Maximum Common Subgraph Problems
Yan-li Liu, Chu-min Li, Hua Jiang +1
Branch-and-bound (BnB) algorithms are widely used to solve combinatorial problems, and the performance crucially depends on its branching heuristic.In this work, we consider a typi…
Clause Vivification by Unit Propagation in CDCL SAT Solvers
Chu-Min Li, Fan Xiao, Mao Luo +3
Original and learnt clauses in Conflict-Driven Clause Learning (CDCL) SAT solvers often contain redundant literals. This may have a negative impact on performance because redundant…