scholarly journals On the geodetic hull number for complementary prisms II

2020 ◽  
Author(s):  
Diane Castonguay ◽  
Erika Morais Martins Coelho ◽  
Hebert Coelho ◽  
Julliano Nascimento
Keyword(s):  
Convex Hull ◽  
Shortest Path ◽  
Disjoint Union ◽  
Perfect Matching ◽  
Convex Set ◽  
Upper Bounds ◽  
The Other ◽  
Lower And Upper Bounds ◽  
Split Graph ◽  
Complementary Prism

In the geodetic convexity, a set of vertices $S$ of a graph $G$ is \textit{convex} if all vertices belonging to any shortest path between two vertices of $S$ lie in $S$. The \textit{convex hull} $H(S)$ of $S$ is the smallest convex set containing $S$. If $H(S) = V(G)$, then $S$ is a \textit{hull set}. The cardinality $h(G)$ of a minimum hull set of $G$ is the \textit{hull number} of $G$. The \textit{complementary prism} $G\overline{G}$ of a graph $G$ arises from the disjoint union of the graph $G$ and $\overline{G}$ by adding the edges of a perfect matching between the corresponding vertices of $G$ and $\overline{G}$. A graph $G$ is \textit{autoconnected} if both $G$ and $\overline{G}$ are connected. Motivated by previous work, we study the hull number for complementary prisms of autoconnected graphs. When $G$ is a split graph, we present lower and upper bounds showing that the hull number is unlimited. In the other case, when $G$ is a non-split graph, it is limited by $3$.

scholarly journals On the General Position Number of Complementary Prisms

2021 ◽  
Vol 178 (3) ◽  
pp. 267-281
Author(s):  
P. K. Neethu ◽  
S.V. Ullas Chandran ◽  
Manoj Changat ◽  
Sandi Klavžar
Keyword(s):  
Lower Bound ◽  
Bipartite Graph ◽  
General Position ◽  
Disjoint Union ◽  
Perfect Matching ◽  
Split Graph ◽  
Block Graphs ◽  
Disconnected Graphs ◽  
Complementary Prism ◽  
Sharp Lower Bound

The general position number gp(G) of a graph G is the cardinality of a largest set of vertices S such that no element of S lies on a geodesic between two other elements of S. The complementary prism G G ¯ of G is the graph formed from the disjoint union of G and its complement G ¯ by adding the edges of a perfect matching between them. It is proved that gp(G G ¯ ) ≤ n(G) + 1 if G is connected and gp(G G ¯ ) ≤ n(G) if G is disconnected. Graphs G for which gp(G G ¯ ) = n(G) + 1 holds, provided that both G and G ¯ are connected, are characterized. A sharp lower bound on gp(G G ¯ ) is proved. If G is a connected bipartite graph or a split graph then gp(G G ¯ ) ∈ {n(G), n(G)+1}. Connected bipartite graphs and block graphs for which gp(G G ¯ ) = n(G) + 1 holds are characterized. A family of block graphs is constructed in which the gp-number of their complementary prisms is arbitrary smaller than their order.

scholarly journals Regarding Two Conjectures on Clique and Biclique Partitions

10.37236/9564 ◽  
2021 ◽  
Vol 28 (4) ◽  
Author(s):  
Dhruv Rohatgi ◽  
John C. Urschel ◽  
Jake Wellens
Keyword(s):  
Upper Bounds ◽  
The Other ◽  
Lower And Upper Bounds ◽  
Minimum Number ◽  
Other Regarding

For a graph $G$, let $cp(G)$ denote the minimum number of cliques of $G$ needed to cover the edges of $G$ exactly once. Similarly, let $bp_k(G)$ denote the minimum number of bicliques (i.e. complete bipartite subgraphs of $G$) needed to cover each edge of $G$ exactly $k$ times. We consider two conjectures – one regarding the maximum possible value of $cp(G) + cp(\overline{G})$ (due to de Caen, Erdős, Pullman and Wormald) and the other regarding $bp_k(K_n)$ (due to de Caen, Gregory and Pritikin). We disprove the first, obtaining improved lower and upper bounds on $\max_G cp(G) + cp(\overline{G})$, and we prove an asymptotic version of the second, showing that $bp_k(K_n) = (1+o(1))n$.

SETS OF K-INDEPENDENT STRINGS

2010 ◽  
Vol 21 (03) ◽  
pp. 321-327 ◽  
Author(s):  
YEN-WU TI ◽  
CHING-LUEH CHANG ◽  
YUH-DAUH LYUU ◽  
ALEXANDER SHEN
Keyword(s):  
Information Theory ◽  
Kolmogorov Complexity ◽  
Upper Bounds ◽  
The Other ◽  
Lower And Upper Bounds ◽  
Algorithmic Information Theory ◽  
Algorithmic Information ◽  
Conditional Complexity

A bit string is random (in the sense of algorithmic information theory) if it is incompressible, i.e., its Kolmogorov complexity is close to its length. Two random strings are independent if knowing one of them does not simplify the description of the other, i.e., the conditional complexity of each string (using the other as a condition) is close to its length. We may define independence of a k-tuple of strings in the same way. In this paper we address the following question: what is that maximal cardinality of a set of n-bit strings if any k elements of this set are independent (up to a certain constant)? Lower and upper bounds that match each other (with logarithmic precision) are provided.

scholarly journals Lower and upper bounds of shortest paths in reachability graphs

2004 ◽  
Vol 2004 (57) ◽  
pp. 3023-3036 ◽  
Author(s):  
P. K. Mishra
Keyword(s):  
Petri Nets ◽  
Shortest Path ◽  
Petri Net ◽  
Free Choice ◽  
Shortest Paths ◽  
Upper Bounds ◽  
Lower And Upper Bounds ◽  
Marked Graphs ◽  
Marked Graph

We prove the following property for safe marked graphs, safe conflict-free Petri nets, and live and safe extended free-choice Petri nets. We prove the following three results. If the Petri net is a marked graph, then the length of the shortest path is at most(|T|−1)⋅|T|/2. If the Petri net is conflict free, then the length of the shortest path is at most(|T|+1)⋅|T|/2. If the petrinet is live and extended free choice, then the length of the shortest path is at most|T|⋅|T+1|⋅|T+2|/6, whereTis the set of transitions of the net.

scholarly journals On the Information Ratio of Graphs with Many Leaves

2019 ◽  
Vol 73 (1) ◽  
pp. 97-108
Author(s):  
Máté Gyarmati ◽  
Péter Ligeti
Keyword(s):  
Lower Bound ◽  
Secret Sharing ◽  
Complete Graph ◽  
Upper Bounds ◽  
Entropy Method ◽  
The Other ◽  
Secret Sharing Schemes ◽  
Lower And Upper Bounds ◽  
Information Ratio ◽  
Recursive Constructions

Abstract We investigate the information ratio of graph-based secret sharing schemes. This ratio characterizes the efficiency of a scheme measured by the amount of information the participants must remember for each bits in the secret. We examine the information ratio of several systems based on graphs with many leaves, by proving non-trivial lower and upper bounds for the ratio. On one hand, we apply the so-called entropy method for proving that the lower bound for the information ratio of n-sunlet graphs composed of a 1-factor between the vertices of a cycle Cn and n independent vertices is 2. On the other hand, some symmetric and recursive constructions are given that yield the upper bounds. In particular, we show that the information ratio of every graph composed of a 1-factor between a complete graph Kn and at most 4 independent vertices is smaller than 2.

Are You Sure That This Happened? Assessing the Factuality Degree of Events in Text

2012 ◽  
Vol 38 (2) ◽  
pp. 261-299 ◽  
Author(s):  
Roser Saurí ◽  
James Pustejovsky
Keyword(s):  
Computational Model ◽  
Upper Bounds ◽  
Epistemic Modality ◽  
The Other ◽  
Proof Of Concept ◽  
Lower And Upper Bounds ◽  
Information Level ◽  
Two Measures ◽  
The One ◽  
Event Factuality

Identifying the veracity, or factuality, of event mentions in text is fundamental for reasoning about eventualities in discourse. Inferences derived from events judged as not having happened, or as being only possible, are different from those derived from events evaluated as factual. Event factuality involves two separate levels of information. On the one hand, it deals with polarity, which distinguishes between positive and negative instantiations of events. On the other, it has to do with degrees of certainty (e.g., possible, probable), an information level generally subsumed under the category of epistemic modality. This article aims at contributing to a better understanding of how event factuality is articulated in natural language. For that purpose, we put forward a linguistic-oriented computational model which has at its core an algorithm articulating the effect of factuality relations across levels of syntactic embedding. As a proof of concept, this model has been implemented in De Facto, a factuality profiler for eventualities mentioned in text, and tested against a corpus built specifically for the task, yielding an F1 of 0.70 (macro-averaging) and 0.80 (micro-averaging). These two measures mutually compensate for an over-emphasis present in the other (either on the lesser or greater populated categories), and can therefore be interpreted as the lower and upper bounds of the De Facto's performance.

scholarly journals Intrinsic volumes of inscribed random polytopes in smooth convex bodies

2010 ◽  
Vol 42 (3) ◽  
pp. 605-619 ◽  
Author(s):  
I. Bárány ◽  
F. Fodor ◽  
V. Vígh
Keyword(s):  
Convex Body ◽  
Convex Hull ◽  
Convex Bodies ◽  
Upper Bounds ◽  
Smooth Convex ◽  
Lower And Upper Bounds ◽  
Covering Theorem ◽  
Intrinsic Volumes ◽  
Large Numbers ◽  
Distribution Matching

Let K be a d-dimensional convex body with a twice continuously differentiable boundary and everywhere positive Gauss-Kronecker curvature. Denote by Kn the convex hull of n points chosen randomly and independently from K according to the uniform distribution. Matching lower and upper bounds are obtained for the orders of magnitude of the variances of the sth intrinsic volumes Vs(Kn) of Kn for s ∈ {1,…,d}. Furthermore, strong laws of large numbers are proved for the intrinsic volumes of Kn. The essential tools are the economic cap covering theorem of Bárány and Larman, and the Efron-Stein jackknife inequality.

scholarly journals Intrinsic volumes of inscribed random polytopes in smooth convex bodies

2010 ◽  
Vol 42 (03) ◽  
pp. 605-619
Author(s):  
I. Bárány ◽  
F. Fodor ◽  
V. Vígh
Keyword(s):  
Convex Body ◽  
Convex Hull ◽  
Convex Bodies ◽  
Upper Bounds ◽  
Smooth Convex ◽  
Lower And Upper Bounds ◽  
Covering Theorem ◽  
Intrinsic Volumes ◽  
Large Numbers ◽  
Distribution Matching

LetKbe ad-dimensional convex body with a twice continuously differentiable boundary and everywhere positive Gauss-Kronecker curvature. Denote byKnthe convex hull ofnpoints chosen randomly and independently fromKaccording to the uniform distribution. Matching lower and upper bounds are obtained for the orders of magnitude of the variances of thesth intrinsic volumesVs(Kn) ofKnfors∈ {1,…,d}. Furthermore, strong laws of large numbers are proved for the intrinsic volumes ofKn. The essential tools are the economic cap covering theorem of Bárány and Larman, and the Efron-Stein jackknife inequality.

Algorithms for four-machine flowshop scheduling problem with uncertain processing times to minimize makespan

2020 ◽  
Vol 54 (2) ◽  
pp. 529-553 ◽  
Author(s):  
Muberra Allahverdi ◽  
Ali Allahverdi
Keyword(s):  
Upper Bounds ◽  
The Other ◽  
Computational Experiments ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Lower And Upper Bounds ◽  
Significance Level ◽  
Dominance Relations ◽  
Processing Times ◽  
Flowshop Scheduling Problem

We consider the four-machine flowshop scheduling problem to minimize makespan where processing times are uncertain. The processing times are within some intervals, where the only available information is the lower and upper bounds of job processing times. Some dominance relations are developed, and twelve algorithms are proposed. The proposed algorithms first convert the four-machine problem into two stages, then, use the well-known Johnson’s algorithm, known to yield the optimal solution for the two-stage problem. The algorithms also use the developed dominance relations. The proposed algorithms are extensively evaluated through randomly generated data for different numbers of jobs and different gaps between the lower and upper bounds of processing times. Computational experiments indicate that the proposed algorithms perform well. Moreover, the computational experiments reveal that one of the proposed algorithms, Algorithm A7, performs significantly better than the other eleven algorithms for all possible combinations of the number of jobs and the gaps between the lower and upper bounds. More specifically, error percentages of the other eleven algorithms range from 2.3 to 27.7 times that of Algorithm A7. The results have been confirmed by constructing 99% confidence intervals and tests of hypotheses using a significance level of 0.01.

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