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Pawel Komosa

dblp:147/5124 · DBLP profile ↗
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5ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 5 · 1 since 2021Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
3 papers
Graph algorithms and graph theory · 64% Computational complexity · 25% Algorithms and data structures · 11%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Graph algorithms and graph theory › graph theory › graph transformation
graph modification
0.722020
Lower Bounds for the Parameterized Complexity of Minimum Fill-in and Other Completion Problems · ACM Trans. Algorithms 2020
Lower bounds for the parameterized complexity of Minimum Fill-In and other completion problems · SODA 2016
Computational complexity
parameterized complexity
0.722020
Lower Bounds for the Parameterized Complexity of Minimum Fill-in and Other Completion Problems · ACM Trans. Algorithms 2020
Lower bounds for the parameterized complexity of Minimum Fill-In and other completion problems · SODA 2016
Graph algorithms and graph theory
graph separators
0.512021
Randomized Contractions Meet Lean Decompositions · ACM Trans. Algorithms 2021
Graph algorithms and graph theory › graph partitioning
minimum bisection
0.512021
Randomized Contractions Meet Lean Decompositions · ACM Trans. Algorithms 2021
Algorithms and data structures
parameterized algorithms
0.512021
Randomized Contractions Meet Lean Decompositions · ACM Trans. Algorithms 2021
Graph algorithms and graph theory › graph decomposition
tree decomposition
0.512021
Randomized Contractions Meet Lean Decompositions · ACM Trans. Algorithms 2021
Computational complexity › parameterized complexity › kernelization
kernelization lower bounds
0.412020
Lower Bounds for the Parameterized Complexity of Minimum Fill-in and Other Completion Problems · ACM Trans. Algorithms 2020
Graph algorithms and graph theory › graph theory › graph transformation › graph modification
minimum fill-in
0.412020
Lower Bounds for the Parameterized Complexity of Minimum Fill-in and Other Completion Problems · ACM Trans. Algorithms 2020
Graph algorithms and graph theory › graph theory › graph transformation › graph modification
graph triangulation
0.212016
Lower bounds for the parameterized complexity of Minimum Fill-In and other completion problems · SODA 2016

Methods — techniques the papers use, named apart from their topics

randomized contraction · 0.5subexponential-time approximation scheme · 0.2exponential time hypothesis · 0.2
YearPublicationVenuePosition
2021 Randomized Contractions Meet Lean Decompositions
abstract
We show an algorithm that, given an n -vertex graph G and a parameter k , in time 2 O ( k log k ) n O (1) finds a tree decomposition of G with the following properties: — every adhesion of the tree decomposition is of size at most k , and — every bag of the tree decomposition is ( i , i )-unbreakable in G for every 1 ⩽ i ⩽ k . Here, a set X ⊆ V ( G ) is ( a , b )-unbreakable in G if for every separation ( A , B ) of order at most b in G , we have | A \cap X | ⩽ a or | B ∩ X | ⩽ a . The resulting tree decomposition has arguably best possible adhesion size bounds and unbreakability guarantees. Furthermore, the parametric factor in the running time bound is significantly smaller than in previous similar constructions. These improvements allow us to present parameterized algorithms for M INIMUM B ISECTION , S TEINER C UT , and S TEINER M ULTICUT with improved parameteric factor in the running time bound. The main technical insight is to adapt the notion of lean decompositions of Thomas and the subsequent construction algorithm of Bellenbaum and Diestel to the parameterized setting.
Marek Cygan, Pawel Komosa, Daniel Lokshtanov, Marcin Pilipczuk, Michal Pilipczuk, Saket Saurabh 0001, Magnus Wahlström
ACM Trans. Algorithms2
2020 Lower Bounds for the Parameterized Complexity of Minimum Fill-in and Other Completion Problems
abstract
In this work, we focus on several completion problems for subclasses of chordal graphs: M INIMUM F ILL -I N , I NTERVAL C OMPLETION , P ROPER I NTERVAL C OMPLETION , T RIVIALLY P ERFECT C OMPLETION , and T HRESHOLD C OMPLETION . In these problems, the task is to add at most k edges to a given graph to obtain a chordal, interval, proper interval, threshold, or trivially perfect graph, respectively. We prove the following lower bounds for all these problems, as well as for the related C HAIN C OMPLETION problem: • Assuming the Exponential Time Hypothesis, none of these problems can be solved in time 2 O ( n 1/2 /log c n ) or 2 O ( k 1/4 /log c k )· n O (1) , for some integer c . • Assuming the non-existence of a subexponential-time approximation scheme for M IN B ISECTION on d -regular graphs, for some constant d , none of these problems can be solved in time 2 o ( n ) or 2 o √k) }· n O (1) . For all the aforementioned completion problems, apart from P ROPER I NTERVAL C OMPLETION , FPT algorithms with running time of the form 2 O (√ k log k ) · n O (1) are known. Thus, the second result proves that a significant improvement of any of these algorithms would lead to a surprising breakthrough in the design of approximation algorithms for M IN B ISECTION . To prove our results, we use a reduction methodology based on combining the classic approach of starting with a sparse instance of 3-S AT , prepared using the Sparsification Lemma, with the existence of almost linear-size Probabilistically Checkable Proofs. Apart from our main results, we also obtain lower bounds excluding the existence of subexponential algorithms for the O PTIMUM L INEAR A RRANGEMENT problem, as well as improved, yet still not tight, lower bounds for F EEDBACK A RC S ET IN T OURNAMENTS .
Ivan Bliznets, Marek Cygan, Pawel Komosa, Michal Pilipczuk, Lukás Mach
ACM Trans. Algorithms3
2016 Hardness of Approximation for H-Free Edge Modification Problems
abstract
The H-free Edge Deletion problem asks, for a given graph G and integer k, whether it is possible to delete at most k edges from G to make it H-free, that is, not containing H as an induced subgraph. The H-free Edge Completion problem is defined similarly, but we add edges instead of deleting them. The study of these two problem families has recently been the subject of intensive studies from the point of view of parameterized complexity and kernelization. In particular, it was shown that the problems do not admit polynomial kernels (under plausible complexity assumptions) for almost all graphs H, with several important exceptions occurring when the class of H-free graphs exhibits some structural properties. In this work we complement the parameterized study of edge modification problems to H-free graphs by considering their approximability. We prove that whenever H is 3-connected and has at least two non-edges, then both H-free Edge Deletion and H-free Edge Completion are very hard to approximate: they do not admit poly(OPT)-approximation in polynomial time, unless P=NP, or even in time subexponential in OPT, unless the Exponential Time Hypothesis fails. The assumption of the existence of two non-edges appears to be important: we show that whenever H is a complete graph without one edge, then H-free Edge Deletion is tightly connected to the \minhorn problem, whose approximability is still open. Finally, in an attempt to extend our hardness results beyond 3-connected graphs, we consider the cases of H being a path or a cycle, and we achieve an almost complete dichotomy there.
Ivan Bliznets, Marek Cygan, Pawel Komosa, Michal Pilipczuk
APPROX-RANDOM3
2016 Lower bounds for the parameterized complexity of Minimum Fill-In and other completion problems
abstract
In this work, we focus on several completion problems for subclasses of chordal graphs: Minimum Fill-In, Interval Completion, Proper Interval Completion, Threshold Completion, and Trivially Perfect Completion. In these problems, the task is to add at most k edges to a given graph in order to obtain a chordal, interval, proper interval, threshold, or trivially perfect graph, respectively. We prove the following lower bounds for all these problems, as well as for the related Chain Completion problem: Assuming the Exponential Time Hypothesis, none of these problems can be solved in time 2O(n1/2/logcn) or 2O(k1/4/logck). nO(1) for some integer c. Assuming the non-existence of a subexponential-time approximation scheme for Min Bisection on d-regular graphs, for some constant d, none of these problems can be solved in time 2o(n) or .
Ivan Bliznets, Marek Cygan, Pawel Komosa, Lukás Mach, Michal Pilipczuk
SODA3
2015 Kernelization lower bound for Permutation Pattern Matching
Ivan Bliznets, Marek Cygan, Pawel Komosa, Lukás Mach
Inf. Process. Lett.3