On the Recognition of Strong-Robinsonian Incomplete Matrices

TL;DR

This paper investigates the recognition problem for incomplete Strong-Robinsonian matrices, proving its NP-Completeness and providing a parameterized algorithm based on missing entries and value diversity.

Contribution

It establishes the non-equivalence of Robinson and Strong-Robinson matrices and introduces an efficient recognition algorithm parameterized by missing entries.

Findings

Recognition of incomplete Strong-Robinsonian matrices is NP-Complete.

A parameterized $O(|w|^b n^2)$ recognition algorithm is proposed.

Not all incomplete Robinson matrices are Strong-Robinsonian, showing their definitions differ.

Abstract

A matrix is incomplete when some of its entries are missing. A Robinson incomplete symmetric matrix is an incomplete symmetric matrix whose non-missing entries do not decrease along rows and columns when moving toward the diagonal. A Strong-Robinson incomplete symmetric matrix is an incomplete symmetric matrix $A$ such that $a_{k,l} \geq a_{i,j}$ if $a_{i,j}$ and $a_{k,l}$ are two non-missing entries of $A$ and $i\leq k \leq l \leq j$. On the other hand, an incomplete symmetric matrix is Strong-Robinsonian if there is a simultaneous reordering of its rows and columns that produces a Strong-Robinson matrix. In this document, we first show that there is an incomplete Robinson matrix which is not Strong-Robinsonian. Therefore, these two definitions are not equivalent. Secondly, we study the recognition problem for Strong-Robinsonian incomplete matrices. It is known that recognition of incomplete Robinsonian matrices is NP-Complete. We show that the recognition of incomplete Strong-Robinsonian matrices is also NP-Complete. However, we show that recognition of Strong-Robinsonian matrices can be parametrized with respect to the number of missing entries. Indeed, we present an $O(|w|^bn^2)$ recognition algorithm for Strong-Robinsonian matrices, where $b$ is the number of missing entries, $n$ is the size of the matrix, and $|w|$ is the number of different values in the matrix.