Matrix Encoding Networks for Neural Combinatorial Optimization
arXiv:2106.11113
Abstract
Machine Learning (ML) can help solve combinatorial optimization (CO) problems better. A popular approach is to use a neural net to compute on the parameters of a given CO problem and extract useful information that guides the search for good solutions. Many CO problems of practical importance can be specified in a matrix form of parameters quantifying the relationship between two groups of items. There is currently no neural net model, however, that takes in such matrix-style relationship data as an input. Consequently, these types of CO problems have been out of reach for ML engineers. In this paper, we introduce Matrix Encoding Network (MatNet) and show how conveniently it takes in and processes parameters of such complex CO problems. Using an end-to-end model based on MatNet, we solve asymmetric traveling salesman (ATSP) and flexible flow shop (FFSP) problems as the earliest neural approach. In particular, for a class of FFSP we have tested MatNet on, we demonstrate a far superior empirical performance to any methods (neural or not) known to date.
arXiv commentAccepted at NeurIPS 2021
References in corpus (8)
- Sequence to Sequence Learning with Neural Networks
- Learning Combinatorial Optimization Algorithms over Graphs
- Neural Combinatorial Optimization with Reinforcement Learning
- An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
- Attention, Learn to Solve Routing Problems!
- Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
- Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem
- Multi-Agent Routing Value Iteration Network