The spectral characterizations of the connected multicone graphs Kw ▽ LHS and Kw ▽ LGQ(3,9)

2018 ◽  
Vol 10 (02) ◽  
pp. 1850019 ◽  
Author(s):  
Ali Zeydi Abdian ◽  
S. Morteza Mirafzal
Keyword(s):  
Optimization Problems ◽  
Spectral Graph Theory ◽  
Combinatorial Optimization Problems ◽  
Spectral Graph ◽  
The Past ◽  
Laplacian Spectra ◽  
Signless Laplacian ◽  
Multicone Graph ◽  
Spectral Characterizations ◽  
Sims Graph

In the past decades, graphs that are determined by their spectrum have received much more and more attention, since they have been applied to several fields, such as randomized algorithms, combinatorial optimization problems and machine learning. An important part of spectral graph theory is devoted to determining whether given graphs or classes of graphs are determined by their spectra or not. So, finding and introducing any class of graphs which are determined by their spectra can be an interesting and important problem. The main aim of this study is to characterize two classes of multicone graphs which are determined by their adjacency, Laplacian and signless Laplacian spectra. A multicone graph is defined to be the join of a clique and a regular graph. Let [Formula: see text] denote a complete graph on [Formula: see text] vertices. In the paper, we show that multicone graphs [Formula: see text] and [Formula: see text] are determined by both their adjacency spectra and their Laplacian spectra, where [Formula: see text] and [Formula: see text] denote the Local Higman–Sims graph and the Local [Formula: see text] graph, respectively. In addition, we prove that these multicone graphs are determined by their signless Laplacian spectra.

scholarly journals On the Adjacency, Laplacian, and Signless Laplacian Spectrum of Coalescence of Complete Graphs

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
S. R. Jog ◽  
Raju Kotambari
Keyword(s):  
Line Graph ◽  
Laplacian Matrix ◽  
Spectral Graph Theory ◽  
Complete Graphs ◽  
Laplacian Spectrum ◽  
Spectral Graph ◽  
Signless Laplacian Spectrum ◽  
Subdivision Graph ◽  
Laplacian Energy ◽  
Signless Laplacian

Coalescence as one of the operations on a pair of graphs is significant due to its simple form of chromatic polynomial. The adjacency matrix, Laplacian matrix, and signless Laplacian matrix are common matrices usually considered for discussion under spectral graph theory. In this paper, we compute adjacency, Laplacian, and signless Laplacian energy (Qenergy) of coalescence of pair of complete graphs. Also, as an application, we obtain the adjacency energy of subdivision graph and line graph of coalescence from itsQenergy.

scholarly journals Towards a spectral theory of graphs based on the signless Laplacian, I

2009 ◽  
Vol 85 (99) ◽  
pp. 19-33 ◽  
Author(s):  
Dragos Cvetkovic ◽  
Slobodan Simic
Keyword(s):  
Graph Theory ◽  
Spectral Theory ◽  
Spectral Graph Theory ◽  
Line Graphs ◽  
Regular Graphs ◽  
Spectral Graph ◽  
Q Theory ◽  
Signless Laplacian ◽  
M Theory ◽  
Theory Of Graphs

A spectral graph theory is a theory in which graphs are studied by means of eigenvalues of a matrix M which is in a prescribed way defined for any graph. This theory is called M-theory. We outline a spectral theory of graphs based on the signless Laplacians Q and compare it with other spectral theories, in particular with those based on the adjacency matrix A and the Laplacian L. The Q-theory can be composed using various connections to other theories: equivalency with A-theory and L-theory for regular graphs, or with L-theory for bipartite graphs, general analogies with A-theory and analogies with A-theory via line graphs and subdivision graphs. We present results on graph operations, inequalities for eigenvalues and reconstruction problems.

Computational Metrology for the Design and Manufacture of Product Geometry: A Classification and Synthesis

2006 ◽  
Vol 7 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Vijay Srinivasan
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Design Specification ◽  
Combinatorial Optimization Problems ◽  
The Past ◽  
Manufactured Products ◽  
Measurement Tools ◽  
The Status ◽  
Computational Metrology ◽  
Design And Manufacture

The increasing use of advanced measurement tools and technology in industry over the past 30 years has ushered in a new set of challenging computational problems. These problems can be broadly classified as fitting and filtering of discrete geometric data collected by measurements made on manufactured products. Collectively, they define the field of computational metrology for the design specification, production, and verification of product geometry. The fitting problems can be posed and solved as optimization problems; they involve both continuous and combinatorial optimization problems. The filtering problems can be unified under convolution problems, which include convolutions of functions as well as convolutions of sets. This paper presents the status of research and standardization efforts in computational metrology, with an emphasis on its classification and synthesis.

scholarly journals Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

2021 ◽  
Vol 11 (14) ◽  
pp. 6449
Author(s):  
Fernando Peres ◽  
Mauro Castelli
Keyword(s):  
Combinatorial Optimization ◽  
Scientific Community ◽  
Optimization Problems ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Combinatorial Optimization Problems ◽  
The Past ◽  
Definition Of ◽  
Problem Solvers ◽  
New Algorithms

In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.

scholarly journals Spectral recognition of graphs

2012 ◽  
Vol 22 (2) ◽  
pp. 145-161 ◽  
Author(s):  
Dragos Cvetkovic
Keyword(s):  
Graph Theory ◽  
Spectral Graph Theory ◽  
Graph Spectra ◽  
Cospectral Graphs ◽  
Combinatorial Optimization Problems ◽  
Spectral Graph ◽  
Spectral Distance ◽  
Spectral Recognition ◽  
Graph Parameters ◽  
Isomorphic Graphs

At some time, in the childhood of spectral graph theory, it was conjectured that non-isomorphic graphs have different spectra, i.e. that graphs are characterized by their spectra. Very quickly this conjecture was refuted and numerous examples and families of non-isomorphic graphs with the same spectrum (cospectral graphs) were found. Still some graphs are characterized by their spectra and several mathematical papers are devoted to this topic. In applications to computer sciences, spectral graph theory is considered as very strong. The benefit of using graph spectra in treating graphs is that eigenvalues and eigenvectors of several graph matrices can be quickly computed. Spectral graph parameters contain a lot of information on the graph structure (both global and local) including some information on graph parameters that, in general, are computed by exponential algorithms. Moreover, in some applications in data mining, graph spectra are used to encode graphs themselves. The Euclidean distance between the eigenvalue sequences of two graphs on the same number of vertices is called the spectral distance of graphs. Some other spectral distances (also based on various graph matrices) have been considered as well. Two graphs are considered as similar if their spectral distance is small. If two graphs are at zero distance, they are cospectral. In this sense, cospectral graphs are similar. Other spectrally based measures of similarity between networks (not necessarily having the same number of vertices) have been used in Internet topology analysis, and in other areas. The notion of spectral distance enables the design of various meta-heuristic (e.g., tabu search, variable neighbourhood search) algorithms for constructing graphs with a given spectrum (spectral graph reconstruction). Several spectrally based pattern recognition problems appear in many areas (e.g., image segmentation in computer vision, alignment of protein-protein interaction networks in bio-informatics, recognizing hard instances for combinatorial optimization problems such as the travelling salesman problem). We give a survey of such and other graph spectral recognition techniques used in computer sciences.

scholarly journals NEW UPPER BOUND ON THE LARGEST LAPLACIAN EIGENVALUE OF GRAPHS

2020 ◽  
pp. 533
Author(s):  
Hassan Taheri ◽  
Gholam Hossein Fath-Tabar
Keyword(s):  
Randomized Algorithms ◽  
Optimization Problems ◽  
Undirected Graph ◽  
Minimum Degree ◽  
Laplacian Spectrum ◽  
Lower And Upper Bounds ◽  
Laplacian Eigenvalue ◽  
Combinatorial Optimization Problems ◽  
The Past ◽  
Maximum And Minimum Degree

Let G = (V;E) be a simple, undirected graph with maximum and minimum degree ∆ and respectively, and let A be the adjacency matrix and Q be the Laplacianmatrix of G. In the past decades, the Laplacian spectrum has received much more and more attention, since it has been applied to several elds, such as randomized algorithms, combinatorial optimization problems and machine learning. In this paper, we compute lower and upper bounds for the largest Laplacian eigenvalue which is related with a given maximum and minimum degree and a given number of vertices and edges. We also compare our results in this paper with some known results.

scholarly journals An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 225
Author(s):  
José García ◽  
Gino Astorga ◽  
Víctor Yepes
Keyword(s):  
Transfer Functions ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Set Covering ◽  
Covering Problem ◽  
Perturbation Operator ◽  
K Nearest Neighbors ◽  
Combinatorial Optimization Problems ◽  
Box Plots ◽  
Metaheuristic Techniques

The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.

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