English

Family Column Generation: A Principled Stabilized Column Generation Approach

Data Structures and Algorithms 2021-03-30 v1

Abstract

We tackle the problem of accelerating column generation (CG) approaches to set cover formulations in operations research. At each iteration of CG we generate a dual solution that approximately solves the LP over all columns consisting of a subset of columns in the nascent set. We refer to this linear program (LP) as the Family Restricted Master Problem (FRMP), which provides a tighter bound on the master problem at each iteration of CG, while preserving efficient inference. For example, in the single source capacitated facility location problem (SSCFLP) the family of a column ll associated with facility ff and customer set NlN_l contains the set of columns associated with ff and the customer set that lies in the power set of NlN_l. The solution to FRMP optimization is attacked with a coordinate ascent method in the dual. The generation of direction of travel corresponds to solving the restricted master problem over columns corresponding to the reduced lowest cost column in each family given specific dual variables based on the incumbent dual, and is easily generated without resolving complex pricing problems. We apply our algorithm to the SSCFLP and demonstrate improved performance over two relevant baselines.

Cite

@article{arxiv.2103.15234,
  title  = {Family Column Generation: A Principled Stabilized Column Generation Approach},
  author = {Naveen Haghani and Julian Yarkony and Amelia Regan},
  journal= {arXiv preprint arXiv:2103.15234},
  year   = {2021}
}

Comments

30 pages, 3 figures

R2 v1 2026-06-24T00:37:46.579Z