Search arXiv⌕ Search

arXiv · 2610.02275

Maximum Edge Open Packing on AT-Free, Chordal, and Convex Bipartite Graphs

Abstract

An edge open packing is a set of edges whose endpoints induce a disjoint union of stars. We study the problem of finding a maximum edge open packing in AT-free, chordal, and convex bipartite graphs. For AT-free graphs, we use the oriented star-conflict graph $A_G$, introduced by Das and Santra, for which $ρ_e^o(G)=α(A_G)$, and prove that $A_G$ is AT-free whenever $G$ is AT-free. Consequently, a known maximum independent set algorithm for AT-free graphs yields an $O(n^2+m^4)$-time algorithm for Maximum Edge Open Packing, where $n$ and $m$ denote the numbers of vertices and edges of $G$, respectively. For chordal graphs, we develop an $O(n^4)$-time dynamic programming algorithm over a nice tree decomposition derived from a clique tree, exploiting the structural fact that the endpoint set of an edge open packing intersects every clique in at most two vertices. Finally, for convex bipartite graphs, we obtain an $O(n^4)$-time dynamic programming algorithm based on two boundary indices that separate consecutive star components. This result extends the previously known algorithm for biconvex bipartite graphs and, in particular, provides an improved explicit running-time bound for that class.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gautam K. Das, Kamal Santra. 2026-10-01. Maximum Edge Open Packing on AT-Free, Chordal, and Convex Bipartite Graphs. https://arxiv.org/abs/2610.02275

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

The Two-Dimensional Majority Rule is P-Complete

We prove that prediction for the synchronous two-dimensional majority rule is $\mathrm{P}$-complete under logspace many-one reductions, resolving a problem open for almost three decades. In 1997, Moore established $\mathrm{P}$-completeness in dimension three and higher and conjectured that the two-dimensional case admits an efficient parallel algorithm. We consider an $n\times n$ torus on which each cell follows the majority of its four nearest neighbors and retains its current state in a tie. Given an explicitly specified initial configuration and a time $T$, prediction asks whether a designated cell is in state $+1$ at time $T$. The central challenge is to make independent information streams cross in the plane under a homogeneous, monotone, diffusive local rule. We overcome this obstacle through a temporal encoding of Boolean values: both values generate activity, but are distinguished by signal arrival times. This encoding yields a crossover that preserves both values and composes with wires, duplication, and AND and OR gates to simulate arbitrary monotone Boolean circuits. Thus a local rule that favors agreement can nevertheless transport, combine, and cross independent information in two dimensions. We also prove $\mathrm{P}$-completeness for deciding whether a designated cell ever reaches $+1$, without a prescribed time horizon. The prediction result extends to every uniform symmetric signed majority rule on the same neighborhood, including the minority rule.

cs.DM↗

A Row-wise Algorithm for Graph Realization

Given a $\{0, 1\}$-matrix $M$, the graph realization problem for $M$ asks if there exists a spanning forest such that the columns of $M$ are incidence vectors of paths in the forest. The problem is closely related to the recognition of network matrices, which are a large subclass of totally unimodular matrices and have many applications in mixed-integer programming. Existing efficient algorithms for graph realization grow a submatrix in a column-wise fashion whilst maintaining a graphic realization. In the context of mixed-integer linear programming, this limits the set of submatrices of the constraint matrix that can efficiently be determined to be network matrices to network submatrices that span all rows and a subset of the columns. This paper complements the existing work by providing an algorithm that works in a row-wise fashion and uses similar data structures, and enables the detection of arbitrary graphic submatrices. The main challenge in designing efficient algorithms for the graph realization problem is ambiguity as there may exist many graphs realizing $M$. The key insight for designing an efficient row-wise algorithm is that a graphic matrix is uniquely represented by an SPQR-tree, a graph decomposition that stores all graphs with the same set of cycles. The developed row-wise algorithm uses data structures that are compatible with the column-wise algorithm and can be combined with the latter to detect maximal graphic submatrices.

cs.DM↗

Ranking and Rank Aggregation with Matroid Prefix Constraints

We study ranking and rank aggregation under the Kendall tau distance, subject to matroid or flag matroid constraints on prefixes of the output ranking. In the matroid case, the top-$k$ prefix is required to form a base of a matroid; in the flag matroid case, several prescribed prefixes are required to form bases of a sequence of matroids linked by quotient relations. This framework contains the previously studied notions of $k$-fairness and block-fairness as special cases, and also captures more general hierarchical and assignment-type lower- and upper-quota constraints. We provide a polynomial-time algorithm for finding, given a single input ranking, a closest feasible ranking under flag matroid prefix constraints. The algorithm is a natural greedy procedure, and its optimality is proved via a Bruhat order argument on the symmetric group. As a consequence, existing approximation frameworks for fair rank aggregation carry over to the matroidal setting. We also prove that rank aggregation with matroid constraints is NP-hard for every fixed number $m\ge 2$ of input rankings, even under partition matroid constraints.

cs.DM↗