16 citations · 18 across the 5 of their papers we have counts for
5 papers
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Runzhong Wang, Zhigang Hua, Gan Liu +6
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuris…
Learning to Schedule DAG Tasks
Zhigang Hua, Feng Qi, Gan Liu +1
Scheduling computational tasks represented by directed acyclic graphs (DAGs) is challenging because of its complexity. Conventional scheduling algorithms rely heavily on simple heu…
Learning (Re-)Starting Solutions for Vehicle Routing Problems
Xingwen Zhang, Shuang Yang
A key challenge in solving a combinatorial optimization problem is how to guide the agent (i.e., solver) to efficiently explore the enormous search space. Conventional approaches o…
Variational Optimization for the Submodular Maximum Coverage Problem
Jian Du, Zhigang Hua, Shuang Yang
We examine the \emph{submodular maximum coverage problem} (SMCP), which is related to a wide range of applications. We provide the first variational approximation for this problem…
Solving Billion-Scale Knapsack Problems
Xingwen Zhang, Feng Qi, Zhigang Hua +1
Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale. This paper examines KPs in a slightl…