arXiv subjects
Publications and source records attributed to Dekel Tsur.
In the 3-Hitting Set problem, the input is a hypergraph $G$ such that the size of every hyperedge of $G$ is at most 3, and an integers $k$, and the goal is to decide whether there is a set $S$ of at most $k$ vertices such that every hyperedge of $G$ contains at least one vertex from $S$. In this paper we give an $O^*(2.0409^k)$-time algorithm for 3-Hitting Set.
In the Cograph Deletion (resp., Cograph Editing) problem the input is a graph $G$ and an integer $k$, and the goal is to decide whether there is a set of edges of size at most $k$ whose removal from $G$ (resp., removal and addition to $G$) results in a graph that does not contain an induced path with four vertices. In this paper we give algorithms for Cograph Deletion and Cograph Editing whose running times are $O^*(2.303^k)$ and $O^*(4.329^k)$, respectively.
In the Bicluter Editing problem the input is a graph $G$ and an integer $k$, and the goal is to decide whether $G$ can be transformed into a bicluster graph by adding and removing at most $k$ edges. In this paper we give an algorithm for Bicluster Editing whose running time is $O^*(3.116^k)$.
In the {Claw,Diamond}-Free Edge Deletion problem the input is a graph $G$ and an integer $k$, and the goal is to decide whether there is a set of edges of size at most $k$ such that removing the edges of the set from $G$ results a graph that does not contain an induced claw or diamond. In this paper we give an algorithm for this problem whose running time is $O^*(3.562^k)$.
In the $K_t$-free edge deletion problem, the input is a graph $G$ and an integer $k$, and the goal is to decide whether there is a set of at most $k$ edges of $G$ whose removal results a graph with no clique of size $t$. In this paper we give a kernel to this problem with $O(k^{t-1})$ vertices and edges.
In the Split to Block Vertex Deletion and Split to Threshold Vertex Deletion problems the input is a split graph $G$ and an integer $k$, and the goal is to decide whether there is a set $S$ of at most $k$ vertices such that $G-S$ is a block graph and $G-S$ is a threshold graph, respectively. In this paper we give algorithms for these problems whose running times are $O^*(2.076^k)$ and $O^*(2.733^k)$, respectively.
We study the puzzle game Buttons and Scissors in which the goal is to remove all buttons from an $n\times m$ grid by a series of horizontal and vertical cuts. We show that the corresponding parameterized problem has an algorithm with time complexity $2^{O(k^2 \log k)} (n+m)^{O(1)}$, where $k$ is an upper bound on the number of cuts.
In the Cluster Deletion problem the input is a graph $G$ and an integer $k$, and the goal is to decide whether there is a set of at most $k$ edges whose removal from $G$ results a graph in which every connected component is a clique. In this paper we give an algorithm for Cluster Deletion whose running time is $O^*(1.404^k)$.
In the $l$-path vertex cover problem the input is an undirected graph $G$ and an integer $k$. The goal is to decide whether there is a set of vertices $S$ of size at most $k$ such that $G-S$ does not contain a path with $l$ vertices. In this paper we give parameterized algorithms for $l$-path vertex cover for $l = 5,6,7$, whose time complexities are $O^*(3.945^k)$, $O^*(4.947^k)$, and $O^*(5.951^k)$, respectively.
In the k-Path problem, the input is a directed graph $G$ and an integer $k\geq 1$, and the goal is to decide whether there is a simple directed path in $G$ with exactly $k$ vertices. We give a deterministic algorithm for k-Path with time complexity $O^*(2.554^k)$. This improves the previously best deterministic algorithm for this problem of Zehavi [ESA 2015] whose time complexity is $O^*(2.597^k)$. The technique used by our algorithm can also be used to obtain faster deterministic algorithms for k-Tree, r-Dimensional k-Matching, Graph Motif, and Partial Cover.
In the Cluster Vertex Deletion problem the input is a graph $G$ and an integer $k$. The goal is to decide whether there is a set of vertices $S$ of size at most $k$ such that the deletion of the vertices of $S$ from $G$ results a graph in which every connected component is a clique. We give an algorithm for Cluster Vertex Deletion whose running time is $O^*(1.811^k)$.
A directed graph $G$ is called a pumpkin if $G$ is a union of induced paths with a common start vertex $s$ and a common end vertex $t$, and the internal vertices of every two paths are disjoint. We give an algorithm that given a directed graph $G$ and an integer $k$, decides whether a pumpkin can be obtained from $G$ by deleting at most $k$ vertices. The algorithm runs in $O^*(2^k)$ time.
In the 4-path vertex cover problem, the input is an undirected graph $G$ and an integer $k$. The goal is to decide whether there is a set of vertices $S$ of size at most $k$ such that every path with 4 vertices in $G$ contains at least one vertex of $S$. In this paper we give a parameterized algorithm for 4-path vertex cover whose time complexity is $O^*(2.619^k)$.
In the vertex cover problem, the input is a graph $G$ and an integer $k$, and the goal is to decide whether there is a set of vertices $S$ of size at most $k$ such that every edge of $G$ is incident on at least one vertex in $S$. We study the vertex cover problem on graphs with maximum degree 4 and minimum degree at least 2, parameterized by $r = k-n/3$. We give an algorithm for this problem whose running time is $O^*(1.6253^r)$. As a corollary, we obtain an $O^*(1.2403^k)$-time algorithm for vertex cover on graphs with maximum degree 4.
We give a parameterized algorithm for weighted vertex cover on graphs with maximum degree 3 whose time complexity is $O^*(1.402^t)$, where $t$ is the minimum size of a vertex cover of the input graph.
In the 3-path vertex cover problem, the input is an undirected graph $G$ and an integer $k$. The goal is to decide whether there is a set of vertices $S$ of size at most $k$ such that every path with 3 vertices in $G$ contains at least one vertex of $S$. In this paper we give parameterized algorithm for 3-path cover whose time complexity is $O^*(1.713^k)$. Our algorithm is faster than previous algorithms for this problem.
The problem of aligning two strings A,B in order to determine their similarity is fundamental in the field of pattern matching. An important concept in this domain is the "all scores matrix" that encodes the local alignment comparison of two strings. Namely, let K denote the all scores matrix containing the alignment score of every substring of B with A, and let J denote the all scores matrix containing the alignment score of every suffix of B with every prefix of A. In this paper we consider the problem of maintaining an all scores matrix where the scoring function is the LCS score, while supporting single character prepend and append operations to A and N. Our algorithms exploit the sparsity parameters L=LCS(A,B) and Delta = |B|-L. For the matrix K we propose an algorithm that supports incremental operations to both ends of A in O(Delta) time. Whilst for the matrix J we propose an algorithm that supports a single type of incremental operation, either a prepend operation to A or an append operation to B, in O(L) time. This structure can also be extended to support both operations simultaneously in O(L log log L) time.
We study the problem of storing the minimum number of bits required to answer next/previous larger/smaller value queries on an array $A$ of $n$ numbers, without storing $A$. We show that these queries can be answered by storing at most $3.701 n$ bits. Our result improves the result of Jo and Satti [TCS 2016] that gives an upper bound of $4.088n$ bits for this problem.