RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization

These issues can be more intense in unsupervised tasks Combinatorial optimization is a broad and challenging due to lacking supervision information [27]. In addition, field with real-world applications ranging from traffic routing the large message vectors restrict the scalability because of to recommendation engines. As these problems are often memory overhead. In light of the local search algorithms' NP-hard [1]-[5], an efficient algorithm to find the best performance and the limitation of GNNs, we study how is solution in all instances with feasible resources is unlikely to the performance of a lightweight model directly using node exist. Therefore, researchers have turned to design heuristics features in the cardinality-constrained maximization problems, [5]-[9] in addition to approximation algorithms [10]-[16] then a natural question would be: In light of the local and enumeration [17], [18]. Among many well-known algorithms, search algorithms' performance and the limitation of GNNs, the standard greedy algorithm (Greedy) [19] provides we study how is the performance of a lightweight model the optimal (1 1/e)-approximation ratio for monotone directly using node features in the cardinality-constrained submodular instances, but this theoretical guarantee does maximization problems. Is it possible to design a lightweight not hold for non-submodular functions [20]. The limitation DQN model that can explore solution space like local search of Greedy has led to the development of greedy local (LS) does and serve as a general-purpose algorithm for search techniques that provide a feasible solution for various the combinatorial problem yet remain efficient in terms of applications. These techniques usually allow deletion and exchange runtime and memory consumption?