Extending Robinson Spaces: Complexity and Algorithmic Solutions for Non-Symmetric Dissimilarity Spaces
Discrete Mathematics
2024-12-06 v1 Combinatorics
Abstract
In this work, we extend the concept of Robinson spaces to asymmetric dissimilarities, enhancing their applicability in representing and analyzing complex data. Within this generalized framework, we introduce two different problems that extend the classical seriation problem: an optimization problem and a decision problem. We establish that these problems are NP-hard and NP-complete, respectively. Despite this complexity results, we identify several non-trivial instances where these problems can be solved in polynomial time, providing valuable insights into their tractability.
Cite
@article{arxiv.2412.04118,
title = {Extending Robinson Spaces: Complexity and Algorithmic Solutions for Non-Symmetric Dissimilarity Spaces},
author = {Francois Brucker and Pascal Préa and Christopher Thraves Caro},
journal= {arXiv preprint arXiv:2412.04118},
year = {2024}
}