Many traditional algorithms for solving combinatorial optimization problems involve using
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …

Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

We consider the problem of inferring high-dimensional data $ x $ in a model that consists of
a prior $ p (x) $ and an auxiliary differentiable constraint $ c (x, y) $ on $ x $ given some …

Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

We present NeuroLKH, a novel algorithm that combines deep learning with the strong
traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem …

Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

This paper presents a framework to tackle combinatorial optimization problems using neural
networks and reinforcement learning. We focus on the traveling salesman problem (TSP) …

For the traveling salesman problem (TSP), the existing supervised learning based
algorithms suffer seriously from the lack of generalization ability. To overcome this …

The aim of the study is to provide interesting insights on how efficient machine learning
algorithms could be adapted to solve combinatorial optimization problems in conjunction …