scholarly journals Tight Approximation for Unconstrained XOS Maximization

2021 ◽  
Author(s):  
Yuval Filmus ◽  
Yasushi Kawase ◽  
Yusuke Kobayashi ◽  
Yutaro Yamaguchi
Keyword(s):  
Polynomial Time ◽  
Linear Time ◽  
Exact Algorithm ◽  
Time Algorithm ◽  
Linear Time Algorithm ◽  
Additive Functions ◽  
Set Size ◽  
Approximation Bound ◽  
Set Function

A set function is called XOS if it can be represented by the maximum of additive functions. When such a representation is fixed, the number of additive functions required to define the XOS function is called the width. In this paper, we study the problem of maximizing XOS functions in the value oracle model. The problem is trivial for the XOS functions of width 1 because they are just additive, but it is already nontrivial even when the width is restricted to 2. We show two types of tight bounds on the polynomial-time approximability for this problem. First, in general, the approximation bound is between O(n) and [Formula: see text], and exactly [Formula: see text] if randomization is allowed, where n is the ground set size. Second, when the width of the input XOS functions is bounded by a constant k ≥ 2, the approximation bound is between k − 1 and k − 1 − ɛ for any ɛ > 0. In particular, we give a linear-time algorithm to find an exact maximizer of a given XOS function of width 2, whereas we show that any exact algorithm requires an exponential number of value oracle calls even when the width is restricted to 3.

scholarly journals Linear time algorithm for the conjugacy problem in the first Grigorchuk group

2021 ◽  
pp. 1-18
Author(s):  
Mitra Modi ◽  
Mathew Seedhom ◽  
Alexander Ushakov
Keyword(s):  
Polynomial Time ◽  
Linear Time ◽  
Conjugacy Problem ◽  
Time Algorithm ◽  
Linear Time Algorithm ◽  
Conjugate Element ◽  
Grigorchuk Group

We prove that the conjugacy problem in the first Grigorchuk group [Formula: see text] can be solved in linear time. Furthermore, the problem to decide if a list of elements [Formula: see text] contains a pair of conjugate elements can be solved in linear time. We also show that a conjugator for a pair of conjugate element [Formula: see text] can be found in polynomial time.

scholarly journals Advances on Testing C-Planarity of Embedded Flat Clustered Graphs

2019 ◽  
Vol 30 (02) ◽  
pp. 197-230 ◽  
Author(s):  
Markus Chimani ◽  
Giuseppe Di Battista ◽  
Fabrizio Frati ◽  
Karsten Klein
Keyword(s):  
Polynomial Time ◽  
Spanning Trees ◽  
Linear Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Negative Instance ◽  
Discrete Mathematics ◽  
Connected Components ◽  
Linear Time Algorithm ◽  
Siam Journal

In this paper, we show a polynomial-time algorithm for testing [Formula: see text]-planarity of embedded flat clustered graphs with at most two vertices per cluster on each face. Our result is based on a reduction to the planar set of spanning trees in topological multigraphs (pssttm) problem, which is defined as follows. Given a (non-planar) topological multigraph [Formula: see text] with [Formula: see text] connected components [Formula: see text], do spanning trees of [Formula: see text] exist such that no two edges in any two spanning trees cross? Kratochvíl et al. [SIAM Journal on Discrete Mathematics, 4(2): 223–244, 1991] proved that the problem is NP-hard even if [Formula: see text]; on the other hand, Di Battista and Frati presented a linear-time algorithm to solve the pssttm problem for the case in which [Formula: see text] is a [Formula: see text]-planar topological multigraph [Journal of Graph Algorithms and Applications, 13(3): 349–378, 2009]. For any embedded flat clustered graph [Formula: see text], an instance [Formula: see text] of the pssttm problem can be constructed in polynomial time such that [Formula: see text] is [Formula: see text]-planar if and only if [Formula: see text] admits a solution. We show that, if [Formula: see text] has at most two vertices per cluster on each face, then it can be tested in polynomial time whether the corresponding instance [Formula: see text] of the pssttm problem is positive or negative. Our strategy for solving the pssttm problem on [Formula: see text] is to repeatedly perform a sequence of tests, which might let us conclude that [Formula: see text] is a negative instance, and simplifications, which might let us simplify [Formula: see text] by removing or contracting some edges. Most of these tests and simplifications are performed “locally”, by looking at the crossings involving a single edge or face of a connected component [Formula: see text] of [Formula: see text]; however, some tests and simplifications have to consider certain global structures in [Formula: see text], which we call [Formula: see text]-donuts. If no test concludes that [Formula: see text] is a negative instance of the pssttm problem, then the simplifications eventually transform [Formula: see text] into an equivalent [Formula: see text]-planar topological multigraph on which we can apply the cited linear-time algorithm by Di Battista and Frati.

scholarly journals The reverse total weighted distance problem on networks with variable edge lengths

Filomat ◽  
2021 ◽  
Vol 35 (4) ◽  
pp. 1333-1342
Author(s):  
Kien Nguyen ◽  
Nguyen Hung
Keyword(s):  
Polynomial Time ◽  
Linear Time ◽  
Time Algorithm ◽  
Set Cover ◽  
Linear Time Algorithm ◽  
Iterative Approach ◽  
Weighted Distance ◽  
Set Cover Problem ◽  
Cover Problem ◽  
Distance Problem

We address the problem of reducing the edge lengths of a network within a given budget so that the sum of weighted distances from each vertex to others is minimized. We call this problem the reverse total weighted distance problem on networks. We first show that the problem is NP-hard by reducing the set cover problem to it in polynomial time. Particularly, we develop a linear time algorithm to solve the problem on a tree. For the problem on cycles, we devise an iterative approach without mentioning the exact complexity. Additionally, if the cycle has uniform edge lengths, we can prove that the specified approach runs in O(n3) time as each edge of the cycle can be reduced at most once, where n is the number of vertices in the underlying cycle.

scholarly journals Total Roman {3}-Domination: The Complexity and Linear-Time Algorithm for Trees

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 293
Author(s):  
Xinyue Liu ◽  
Huiqin Jiang ◽  
Pu Wu ◽  
Zehui Shao
Keyword(s):  
Linear Time ◽  
Minimum Weight ◽  
Domination Number ◽  
Time Algorithm ◽  
Simple Graph ◽  
Linear Time Algorithm ◽  
Domination Problem ◽  
Np Complete ◽  
Chordal Bipartite Graphs ◽  
Dominating Function

For a simple graph G=(V,E) with no isolated vertices, a total Roman {3}-dominating function(TR3DF) on G is a function f:V(G)→{0,1,2,3} having the property that (i) ∑w∈N(v)f(w)≥3 if f(v)=0; (ii) ∑w∈N(v)f(w)≥2 if f(v)=1; and (iii) every vertex v with f(v)≠0 has a neighbor u with f(u)≠0 for every vertex v∈V(G). The weight of a TR3DF f is the sum f(V)=∑v∈V(G)f(v) and the minimum weight of a total Roman {3}-dominating function on G is called the total Roman {3}-domination number denoted by γt{R3}(G). In this paper, we show that the total Roman {3}-domination problem is NP-complete for planar graphs and chordal bipartite graphs. Finally, we present a linear-time algorithm to compute the value of γt{R3} for trees.

A Linear time algorithm for deciding security

1976 ◽  
Author(s):  
A. K. Jones ◽  
R. J. Lipton ◽  
L. Snyder
Keyword(s):  
Linear Time ◽  
Time Algorithm ◽  
Linear Time Algorithm

AN IMPROVED ALGORITHM FOR FINDING TREE DECOMPOSITIONS OF SMALL WIDTH

2000 ◽  
Vol 11 (03) ◽  
pp. 365-371 ◽  
Author(s):  
LJUBOMIR PERKOVIĆ ◽  
BRUCE REED
Keyword(s):  
Linear Time ◽  
Time Algorithm ◽  
Tree Decomposition ◽  
Linear Time Algorithm ◽  
Input Graph ◽  
Primary Motivation ◽  
Small Width ◽  
Tree Decompositions ◽  
Improved Algorithm

We present a modification of Bodlaender's linear time algorithm that, for constant k, determine whether an input graph G has treewidth k and, if so, constructs a tree decomposition of G of width at most k. Our algorithm has the following additional feature: if G has treewidth greater than k then a subgraph G′ of G of treewidth greater than k is returned along with a tree decomposition of G′ of width at most 2k. A consequence is that the fundamental disjoint rooted paths problem can now be solved in O(n2) time. This is the primary motivation of this paper.

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