arXiv subjects

Mirza Redzic

Publications and source records attributed to Mirza Redzic.

7 recordsLinked to original sources

arXiv2026

When Does Sparsity Help for k-Independent Set in Hypergraphs and Other Boolean CSPs?

Consider the fundamental task of finding independent sets of (constant) size $k$ in a given $n$-node hypergraph. How is the time complexity affected by the sparsity of the input, i.e., the number of hyperedges $m$? Tur\'{a}n's theorem implies that the problem is trivial if $m=O(n^{2-\epsilon})$ for some $\epsilon> 0$. Above that threshold (i.e., if $m=\Theta(n^\gamma)$ for some $\gamma \ge 2$), we give a perhaps surprising algorithm with running time $O\left(\min\left\{n^{\frac{\omega}{3}k} + m^{k/3}, n^k\right\}\right)$ (for $k$ divisible by 3), which is essentially conditionally optimal for all $\gamma\ge 2$, assuming the $k$-clique and 3-uniform hyperclique hypotheses (here, $\omega<2.372$ denotes the matrix multiplication exponent). In fact, we obtain a more detailed time complexity, sensitive to the arity distribution of the hyperedges. To study such phenomena in more generality, we study the time complexity of finding solutions of (constant) size $k$ in sparse instances of Boolean constraint satisfaction problems, where $n$ and $m$ denote the number of variables and constraints. Our results include an essentially full classification of the influence of sparsity for Boolean constraint families of binary arity. Of particular technical interest is a conditionally tight algorithm for the family consisting of the binary NAND and Implication constraints, with a running time of $\Theta(m^{\omega k/6 \pm c})$. Further, we identify a large class of constraint families $F$ that exhibits a sharp phase transition: there is a threshold $\gamma_F$ such that the problem is trivial for $m=O(n^{\gamma_F-\epsilon})$, but requires essentially brute-force running time $\Theta(n^{k\pm c})$ for $m=\Omega(n^{\gamma_F})$, assuming the 3-uniform hyperclique hypothesis. Notably, in many cases the combination of constraints display higher time complexity than either constraint alone.

cs.CC↗

arXiv2026

Conditionally Tight Algorithms for Maximum k-Coverage and Partial k-Dominating Set via Arity-Reducing Hypercuts

We revisit the classic Maximum $k$-Coverage problem: Determine the largest number $t$ of elements that can be covered by choosing $k$ sets from a given family $\mathcal{F} = \{S_1,\dots, S_n\}$ of a size-$u$ universe. A notable special case is Partial $k$-Dominating Set, where one chooses $k$ vertices in a graph to maximize the number of dominated vertices. Extensive research has established strong hardness results for various aspects of Maximum $k$-Coverage, such as tight inapproximability results, $W[2]$-hardness, and a conditionally tight worst-case running time of $n^{k\pm o(1)}$. In this paper we ask: (1) Can this time bound be improved for small $t$, at least for Partial $k$-Dominating Set, ideally to time~$t^{k\pm O(1)}$? (2) More ambitiously, can we even determine the best-possible running time of Maximum $k$-Coverage with respect to the perhaps most natural parameters: the universe size $u$, the maximum set size $s$, and the maximum frequency $f$? We successfully resolve both questions. (1) We give an algorithm that solves Partial $k$-Dominating Set in time $O(nt + t^{\frac{2\omega}{3} k+O(1)})$ if $\omega \ge 2.25$ and time $O(nt+ t^{\frac{3}{2} k+O(1)})$ if $\omega \le 2.25$, where $\omega \le 2.372$ is the matrix multiplication exponent. From this we derive a time bound that is conditionally optimal, regardless of $\omega$, based on the well-established $k$-clique and 3-uniform hyperclique hypotheses from fine-grained complexity. We also obtain matching upper and lower bounds for sparse graphs. To address (2) we design an algorithm for Maximum $k$-Coverage running in time $$ \min \left\{ (f\cdot \min\{\sqrt[3]{u}, \sqrt{s}\})^k + \min\{n,f\cdot \min\{\sqrt{u}, s\}\}^{k\omega/3}, n^k\right\} \cdot g(k)n^{\pm O(1)}, $$ and, surprisingly, further show that this complicated time bound is also conditionally optimal.

cs.DS↗

arXiv2025

Engineering Dominating Patterns: A Fine-grained Case Study

The \emph{Dominating $H$-Pattern} problem generalizes the classical $k$-Dominating Set problem: for a fixed \emph{pattern} $H$ and a given graph $G$, the goal is to find an induced subgraph $S$ of $G$ such that (1) $S$ is isomorphic to $H$, and (2) $S$ forms a dominating set in $G$. Fine-grained complexity results show that on worst-case inputs, any significant improvement over the naive brute-force algorithm is unlikely, as this would refute the Strong Exponential Time Hypothesis. Nevertheless, a recent work by Dransfeld et al. (ESA 2025) reveals some significant improvement potential particularly in \emph{sparse} graphs. We ask: Can algorithms with conditionally almost-optimal worst-case performance solve the Dominating $H$-Pattern, for selected patterns $H$, efficiently on practical inputs? We develop and experimentally evaluate several approaches on a large benchmark of diverse datasets, including baseline approaches using the Glasgow Subgraph Solver (GSS), the SAT solver Kissat, and the ILP solver Gurobi. Notably, while a straightforward implementation of the algorithms -- with conditionally close-to-optimal worst-case guarantee -- performs comparably to existing solvers, we propose a tailored Branch-\&-Bound approach -- supplemented with careful pruning techniques -- that achieves improvements of up to two orders of magnitude on our test instances.

cs.DS↗

arXiv2025

On the Relation Between Treewidth, Tree-Independence Number, and Tree-Chromatic Number of Graphs

We investigate two recently introduced graph parameters, both of which measure the complexity of the tree decompositions of a given graph. Recall that the treewidth ${\rm tw}(G)$ of a graph $G$ measures the largest number of vertices required in a bag of every tree decomposition of $G$. Similarly, the tree-independence number ${\rm tree\textnormal{-}}\alpha(G)$ and the tree-chromatic number ${\rm tree\textnormal{-}}\chi(G)$ measure the largest independence number, respectively the largest chromatic number, required in a bag of every tree decomposition of $G$. Recently, Dallard, Milani\v{c}, and \v{S}torgel asked (JCTB, 2024) whether for all graphs $G$ it holds that ${\rm tw}(G)+1 \leq {\rm tree\textnormal{-}}\alpha(G) \cdot {\rm tree\textnormal{-}}\chi(G)$. We provide a negative answer for this question in a strong form: for every function $f\colon {\mathbb N} \rightarrow {\mathbb N}$, there exists a graph $G$ such that ${\rm tw}(G) > {\rm tree\textnormal{-}}\alpha(G) \cdot f({\rm tree\textnormal{-}}\chi(G))$. On the other hand, we complement this result with an upper bound, by showing that ${\rm tw}(G)+1 \leq {\rm tree\textnormal{-}}\alpha(G)^2 \cdot {\rm tree\textnormal{-}}\chi(G)$ for every graph $G$.

math.CO↗

arXiv2025

Resource Constrained Pathfinding with A* and Negative Weights

Constrained pathfinding is a well-studied, yet challenging network optimisation problem that can be seen in a broad range of real-world applications. Pathfinding with multiple resource limits, which is known as the Resource Constrained Shortest Path Problem (RCSP), aims to plan a cost-optimum path subject to limited usage of resources. Given the recent advances in constrained and multi-criteria search with A*, this paper introduces a new resource constrained search framework on the basis of A* to tackle RCSP in large networks, even in the presence of negative cost and negative resources. We empirically evaluate our new algorithm on a set of large instances and show up to two orders of magnitude faster performance compared to state-of-the-art RCSP algorithms in the literature.

cs.AI↗

arXiv2024

Fine-Grained Complexity of Multiple Domination and Dominating Patterns in Sparse Graphs

The study of domination in graphs has led to a variety of domination problems studied in the literature. Most of these follow the following general framework: Given a graph $G$ and an integer $k$, decide if there is a set $S$ of $k$ vertices such that (1) some inner property $ϕ(S)$ (e.g., connectedness) is satisfied, and (2) each vertex $v$ satisfies some domination property $ρ(S, v)$ (e.g., there is an $s\in S$ that is adjacent to $v$). Since many real-world graphs are sparse, we seek to determine the optimal running time of such problems in both the number $n$ of vertices and the number $m$ of edges in $G$. While the classic dominating set problem admits a rather limited improvement in sparse graphs (Fischer, Künnemann, Redzic SODA'24), we show that natural variants studied in the literature admit much larger speed-ups, with a diverse set of possible running times. Specifically, we obtain conditionally optimal algorithms for: 1) $r$-Multiple $k$-Dominating Set (each vertex must be adjacent to at least $r$ vertices in $S$): If $r\le k-2$, we obtain a running time of $(m/n)^{r} n^{k-r+o(1)}$ that is conditionally optimal assuming the 3-uniform hyperclique hypothesis. In sparse graphs, this fully interpolates between $n^{k-1\pm o(1)}$ and $n^{2\pm o(1)}$, depending on $r$. Curiously, when $r=k-1$, we obtain a randomized algorithm beating $(m/n)^{k-1} n^{1+o(1)}$ and we show that this algorithm is close to optimal under the $k$-clique hypothesis. 2) $H$-Dominating Set ($S$ must induce a pattern $H$). We conditionally settle the complexity of three such problems: (a) Dominating Clique ($H$ is a $k$-clique), (b) Maximal Independent Set of size $k$ ($H$ is an independent set on $k$ vertices), (c) Dominating Induced Matching ($H$ is a perfect matching on $k$ vertices).

cs.DS↗

arXiv2023

The Effect of Sparsity on $k$-Dominating Set and Related First-Order Graph Properties

We revisit $k$-Dominating Set, one of the first problems for which a tight $n^k-o(1)$ conditional lower bound (for $k\ge 3$), based on SETH, was shown (Pătraşcu and Williams, SODA 2007). However, the underlying reduction creates dense graphs, raising the question: how much does the sparsity of the graph affect its fine-grained complexity? We first settle the fine-grained complexity of $k$-Dominating Set in terms of both the number of nodes $n$ and number of edges $m$. Specifically, we show an $mn^{k-2-o(1)}$ lower bound based on SETH, for any dependence of $m$ on $n$. This is complemented by an $mn^{k-2+o(1)}$-time algorithm for all $k\ge 3$. For the $k=2$ case, we give a randomized algorithm that employs a Bloom-filter inspired hashing to improve the state of the art of $n^{ω+o(1)}$ to $m^{ω/2+o(1)}$. If $ω=2$, this yields a conditionally tight bound for all $k\ge 2$. To study if $k$-Dominating Set is special in its sensitivity to sparsity, we consider a class of very related problems. The $k$-Dominating Set problem belongs to a type of first-order definable graph properties that we call monochromatic basic problems. These problems are the natural monochromatic variants of the basic problems that were proven complete for the class FOP of first-order definable properties (Gao, Impagliazzo, Kolokolova, and Williams, TALG 2019). We show that among these problems, $k$-Dominating Set is the only one whose fine-grained complexity decreases in sparse graphs. Only for the special case of reflexive properties, is there an additional basic problem that can be solved faster than $n^{k\pm o(1)}$ on sparse graphs. For the natural variant of distance-$r$ $k$-dominating set, we obtain a hardness of $n^{k-o(1)}$ under SETH for every $r\ge 2$ already on sparse graphs, which is tight for sufficiently large $k$.

cs.DS↗