arXiv subjects

Adrian Miclăuş

Publications and source records attributed to Adrian Miclăuş.

7 recordsLinked to original sources

arXiv2026

Bounded-Density Vertex Deletion Beyond Vertex Cover

We study \textsc{Bounded-Density Vertex Deletion} (BDVD), where one asks whether at most $k$ vertices can be deleted so that every remaining subgraph has density at most a rational input threshold $τ_ρ$. We address the twin-cover and distance-to-cluster cases left open by Raupach, Breitkopf, Herrmann, and Nichterlein (MFCS 2026). We prove fixed-parameter tractability parameterized solely by the twin-cover number, for unrestricted input density. For distance to cluster, we establish fixed-parameter tractability parameterized by $\operatorname{dc}(G)+\lfloor2τ_ρ+1\rfloor$, and complement this with $\mathsf{W[1]}$-hardness parameterized by $\operatorname{dc}(G)$ alone when the target density is part of the input, even for integral targets. Thus distance to cluster alone is fixed-parameter tractable throughout $τ_ρ<1$, while it is insufficient for unrestricted input density. Finally, we settle the combined parameter treewidth plus solution size, an open problem posed by Bazgan, Nichterlein, and Vazquez Alferez (JCSS 2025). We prove that BDVD is $\mathsf{W[1]}$-hard parameterized by $\operatorname{td}(G)+k$, and hence also by $\operatorname{tw}(G)+k$, already for targets strictly between $1$ and $2$.

cs.DS↗

arXiv2026

Structural Complexity of Matching-Match: Dense and Sparse Graphs

The Matching-Match puzzle asks whether the vertices of a fixed graph can be colored so that the multiset of color pairs induced by its edges is exactly a prescribed multiset. We study how the complexity of this realization problem depends on the host graph. On the dense side, we give a polynomial-time algorithm for complete $k$-partite graphs for every fixed number $k$ of parts, with arbitrary precoloring and an arbitrary number of colors. We prove a sharp complement-degree threshold: the problem is polynomial-time solvable when $Δ(\overline G)\le1$, but NP-complete on completely uncolored graphs already when $Δ(\overline G)=2$. This yields a dichotomy for uniform complete multipartite graphs, and connected diameter two already suffices for NP-completeness. We also prove W[1]-hardness on cographs parameterized by the number of colors. On the sparse side, completely uncolored paths and cycles admit a linear-time characterization by Euler trails and circuits, while counting feasible colorings is $\#P$-complete on both classes. Counting is nevertheless polynomial-time solvable on stars and complete graphs, even with arbitrary precoloring. A decomposition-transfer theorem yields NP-completeness already at tree-depth two. A separate path-decomposition reduction gives a maximum-degree threshold between one and two for completely uncolored disconnected host graphs with unrestrictedly many colors. Components with at most two edges are tractable, while a disjoint union of $P_4$'s is NP-complete. Finally, precoloring restores tractability in several cases: star forests are polynomial when every center is precolored, and two broad precoloring regimes on length-two spiders are polynomial even when the number of colors is unbounded.

cs.DS↗

arXiv2026

An Approximation Algorithm for Non-uniform Non-contiguous Translocation Distance

Translocations are genome rearrangement operations that exchange prefixes of two chromosomes. We study the non-uniform non-contiguous translocation distance problem, where every string produced during the computation remains available for reuse. Given an initial set of strings $A$ and a target set $B$, the objective is to produce all strings in $B$ using as few translocations as possible. We present the first polynomial-time approximation algorithm for this problem. For a single target string of length $n$, we obtain an $O(\log n)$-approximation, and we extend the result to arbitrary finite target sets with an $O(\log N)$-approximation, where $N$ is the total length of the targets not already present in the initial set. This resolves the approximability question for the non-uniform non-contiguous case left open by Constantin and Popa (TCS 2025).

cs.DS↗

arXiv2026

NP-Hardness and a Fixed-Parameter Algorithm for Translocation Distance

In this paper we study the genome rearrangements done by translocation events. Genome rearrangements were used to measure evolutionary distance between organisms since 1936 (Dobzhansky and Sturtevant). The chromosomes are represented as strings of DNA and the \emph{translocation operation} is defined as the exchange of prefixes between two strings. This operation results in the creation of two new strings (chromosomes) that can then be utilized in subsequent translocations. A translocation is referred to as \emph{contiguous} if the new strings are produced in a single copy, so each of them can be used in only one subsequent operation. When the words produced by a translocation operation are considered to have an infinite number of copies, the translocation is referred to as \emph{non-contiguous}. If the exchanged prefixes are of equal length, the translocation is called \emph{uniform}. Otherwise, the translocation is termed \emph{non-uniform}. The \emph{translocation distance} between two sets of strings, termed the input set and the target set, represents the minimum number of translocations necessary to obtain all the strings in the target set via translocation operations. We prove that both the non-uniform contiguous and the non-uniform non-contiguous translocation distance problems are NP-hard over arbitrary finite alphabets, where the alphabet is part of the input. For the case in which the target set consists of a single string, we give a fixed-parameter tractable algorithm parameterized by the length of the target string.

cs.DS↗

arXiv2026

Maximum Matching-Match: Hardness and Approximation

In this paper, we study \textsc{MaxMMP}, an optimization variant of the Matching-Match Puzzle introduced by Iburi and Uehara (FUN 2024). Given a graph, a partial vertex coloring, and a multiset of colored sticks, the goal is to complete the coloring and assign the sticks to graph edges so as to maximize the number of satisfied edges. We first prove that \textsc{MaxMMP} is APX-hard by an reduction from \textsc{Max-Cut}. The hardness already holds with two colors, no precolored vertices, and only bichromatic sticks. We then give a simple deterministic $\frac{2}{c(c+1)}$-approximation for completely uncolored instances, improving to $\frac{2}{c(c-1)}$ when all sticks are bichromatic. Next, we obtain a randomized $\frac{1-\frac{1}{e}}{2c}$-approximation for arbitrary instances with $c$ colors by reducing the remaining coloring choices to monotone submodular maximization under a partition matroid. On bipartite graphs, the approximation ratio improves to $\frac{1-\frac{1}{e}}{c}$. For every fixed $c$, we further obtain deterministic $\frac{1}{2c}$ and $\frac{1}{c}$-approximations on general and bipartite graphs, respectively, in time $n^{O(c^2)}$. Finally, for every fixed number of colors, we show that \textsc{MaxMMP} can be solved exactly in time $n^{O(c^2)}$ on trees and on cographs.

cs.DS↗

arXiv2026

Complexity and Algorithms for Unary Translocation Distance

Given a finite set of integers $A$, a \emph{unary translocation} produces a new set $A' = A \cup \{u,v\}$, where $u$ and $v$ are nonnegative integers satisfying $x+y=u+v$ for some $x,y\in A$. For an input set $A$ and a target set $B$, the \emph{unary translocation distance} is the minimum number of unary translocations required to obtain a superset containing $B$. In this paper, we study this problem from both theoretical and computational perspectives. We prove that computing the unary translocation distance is strongly NP-hard, thereby answering an open question raised by \citet{ConstantinMiclausPopa2026UnaryTranslocation}. On the positive side, we give an exact pseudo-polynomial algorithm for every fixed constant value of $|B|$, extending our previous results for $|B|\leq 2$. For arbitrary target sets, we present a $2$-approximation algorithm, an additive $(|B|-1)$-approximation algorithm, and show that the additive algorithm also yields a $3$-approximation. We also propose parameterized algorithms, including algorithms parameterized by the maximum value in the input set together with the optimum distance, and by the maximum value in the target set together with $|B|$. In addition, we propose an integer linear programming formulation that gives an exact mathematical model for the problem, analyze its size, and show that the LP relaxation has integrality gap at least $\frac{4}{3}$. Finally, we report computational experiments comparing the $2$-approximation algorithm, beam search, and simulated annealing. The results show that the approximation algorithm is highly effective in practice and often outperforms the heuristic baselines.

cs.DS↗

arXiv2025

PACE Solver Description: twin_width_fmi

In this paper we present \texttt{twin\_width\_fmi}'s solver for the heuristic track of PACE's 2025 competition on Minimum Dominating Set. As a baseline, we implement \texttt{greedy-ln}, a standard greedy dominating-set heuristic that repeatedly selects the vertex that newly dominates the largest number of currently undominated vertices. We then use this greedy solution as the starting point for a simulated annealing local search: we attempt vertex removals and exchanges and accept worsening moves with decaying probability, in order to escape local minima while preserving domination. Our best-performing component, which we ultimately submitted, is \texttt{hedom5}. The design of \texttt{hedom5} is inspired by recent iterative-greedy style domination heuristics~\cite{IterativeGreedy22} that alternate between constructive steps, pruning, and focused repair rather than relying on a single pass. In \texttt{hedom5}, the input graph is first stored in a compact CSR structure and simplified using fast reductions such as forcing neighbors of leaves and handling isolates. We then run a lazy gain-based greedy stage using a priority queue: each candidate vertex is scored by how many currently undominated vertices its closed neighborhood would newly dominate, and scores are only recomputed when necessary. After this constructive phase, we perform an aggressive backward pruning pass that iterates over the chosen dominators in reverse insertion order and deletes any vertex whose closed neighborhood is still fully dominated by the remaining set. Finally, we run a budgeted 1-swap local improvement step that attempts to replace a dominator by an alternative vertex that covers all of its uniquely covered vertices, thereby reducing the size of the dominating set. A brief safety patch at the end guarantees full domination.

cs.DC↗