Learning Geometric Combinatorial Optimization Problems using Self-attention and Domain Knowledge
This work addresses geometric combinatorial optimization problems for researchers and practitioners, but it is incremental as it builds on existing deep learning approaches with specific enhancements.
The authors tackled geometric combinatorial optimization problems by proposing a neural network model using self-attention and domain knowledge, achieving competitive performance in approximate solutions for Delaunay triangulation, convex hull, and the planar Traveling Salesman problem.
Combinatorial optimization problems (COPs) are an important research topic in various fields. In recent times, there have been many attempts to solve COPs using deep learning-based approaches. We propose a novel neural network model that solves COPs involving geometry based on self-attention and a new attention mechanism. The proposed model is designed such that the model efficiently learns point-to-point relationships in COPs involving geometry using self-attention in the encoder. We propose efficient input and output sequence ordering methods that reduce ambiguities such that the model learns the sequences more regularly and effectively. Geometric COPs involve geometric requirements that need to be satisfied. In the decoder, a new masking scheme using domain knowledge is proposed to provide a high penalty when the geometric requirement of the problem is not satisfied. The proposed neural net is a flexible framework that can be applied to various COPs involving geometry. We conduct experiments to demonstrate the effectiveness of the proposed model for three COPs involving geometry: Delaunay triangulation, convex hull, and the planar Traveling Salesman problem. Our experimental results show that the proposed model exhibits competitive performance in finding approximate solutions for solving these problems.