Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application

Column Generation (CG) is a popular method dedicated to enhancing computational efficiency in large scale Combinatorial Optimization (CO) problems. It reduces the number of decision variables in a problem by solving a pricing problem. For many CO problems, the pricing problem is an Elementary Shortest Path Problem with Resource Constraints (ESPPRC). Large ESPPRC instances are difficult to solve to near-optimality. Consequently, we use a Graph neural Network (GNN) to reduces the size of the ESPPRC such that it becomes computationally tractable with standard solving techniques. Our GNN is trained by Unsupervised Learning and outputs a distribution for the arcs to be retained in the reduced PP. The reduced PP is solved by a local search that finds columns with large reduced costs and speeds up convergence. We apply our method on a set of Capacitated Vehicle Routing Problems with Time Windows and show significant improvements in convergence compared to simple reduction techniques from the literature. For a fixed computational budget, we improve the objective values by over 9\% for larger instances. We also analyze the performance of our CG algorithm and test the generalization of our method to different classes of instances than the training data.

Constructing classification trees using column generation

This paper explores the use of Column Generation (CG) techniques in constructing univariate binary decision trees for classification tasks. We propose a novel Integer Linear Programming (ILP) formulation, based on paths in decision trees. We show that the associated pricing problem is NP-hard and propose a random procedure for column selection. In addition, to speed up column generation, we use a restricted parameter set via a sampling procedure using the well-known CART algorithm. Extensive numerical experiments show that our approach outperforms the state-of-the-art ILP-based algorithms in the recent literature both in computation time and solution quality. We also find better solutions that have higher training and testing accuracy than an optimized version of CART. Furthermore, our approach is capable of handling big data sets with tens of thousands of data rows, unlike other ILP-based algorithms. In addition, our approach has the advantage of being able to easily incorporate different objectives.