Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming

TL;DR

This paper introduces a novel global optimization method using semidefinite programming and branch-and-cut techniques to solve large-scale cardinality-constrained minimum sum-of-squares clustering problems more effectively than existing methods.

Contribution

It develops a scalable SDP relaxation and a tailored branch-and-cut algorithm that significantly improve the solution of large real-world clustering instances with prior knowledge of cluster sizes.

Findings

Successfully solves real-world instances 10 times larger than previous methods.

Introduces a new SDP relaxation that scales better with problem size.

Demonstrates the effectiveness of the combined approach through computational experiments.

Abstract

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, has been recently extended to exploit prior knowledge on the cardinality of each cluster. Such knowledge is used to increase performance as well as solution quality. In this paper, we propose a global optimization approach based on the branch-and-cut technique to solve the cardinality-constrained MSSC. For the lower bound routine, we use the semidefinite programming (SDP) relaxation recently proposed by Rujeerapaiboon et al. [SIAM J. Optim. 29(2), 1211-1239, (2019)]. However, this relaxation can be used in a branch-and-cut method only for small-size instances. Therefore, we derive a new SDP relaxation that scales better with the instance size and the number of clusters. In both cases, we strengthen the bound by adding polyhedral cuts. Benefiting from a tailored branching strategy which enforces pairwise constraints, we reduce the complexity of the problems arising in the children nodes. For the upper bound, instead, we present a local search procedure that exploits the solution of the SDP relaxation solved at each node. Computational results show that the proposed algorithm globally solves, for the first time, real-world instances of size 10 times larger than those solved by state-of-the-art exact methods.