Computational geometry is concerned with the design and analysis of algorithms for
geometrical problems. In addition, other more practically oriented, areas of computer …

We have witnessed recently the development of a fast growing body of research focused on
vehicle routing and scheduling problem structures with time window constraints. It is the aim …

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

This paper introduces a new learning-based approach for approximately solving the
Travelling Salesman Problem on 2D Euclidean graphs. We use deep Graph Convolutional …

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

Training foundation models, such as GPT-3 and PaLM, can be extremely expensive, often
involving tens of thousands of GPUs running continuously for months. These models are …

Recently, deep reinforcement learning (DRL) frameworks have shown potential for solving
NP-hard routing problems such as the traveling salesman problem (TSP) without problem …

In this work, we introduce Graph Pointer Networks (GPNs) trained using reinforcement
learning (RL) for tackling the traveling salesman problem (TSP). GPNs build upon Pointer …

Many real-world problems can be reduced to combinatorial optimization on a graph, where
the subset or ordering of vertices that maximize some objective function must be found. With …

Recent advances in automatic machine learning (aML) allow solving problems without any
human intervention. However, sometimes a human-in-the-loop can be beneficial in solving …