• Communications of the Korean Mathematical Society
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• v.18 no.4
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• pp.745-754
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• 2003

Maximum clique problem is to find a maximum clique(largest in size) in an undirected graph G. We present a method that computes either a maximum clique or an upper bound for the size of a maximum clique in G. We show that this method performs well on certain class of graphs and discuss the application of this method in a branch and bound algorithm for solving maximum clique problem, whose efficiency is depended on the computation of good upper bounds.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.1
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• pp.84-92
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• 1994

In this paper, we present a novel scheduling algorithm using the weighted interval graph. An interval graph is constructed, where an interval is a time frame of each operation. And for each operation type, we look for the maximum clique of the interval graph: the number of nodes of the maximum clique represents the number of operation that are executed concurrently. In order to minimize resource cost. we select the operation type to reduce the number of nodes of a maximum clique. For the selected operation type, an operation selected by selection rule is moved to decrease the number of nodes of a maximum clique. A selected operation among unscheduled operations is moved repeatly and assigned to a control step consequently. The proposed algorithm is applied to the pipeline and the nonpipeline data path synthesis. The experiment for examples shows the efficiency of the proposed scheduling algorithm.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.227-235
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• 2015

In this paper, I propose a linear time algorithm devised to produce exact solution to NP-complete maximum clique problem. The proposed algorithm firstly, from a given graph G=(V,E), sets vertex $v_i$ of the maximum degree ${\Delta}(G)$ as clique's major vertex. It then selects vertex $v_j$ of ${\Delta}(G)$ among vertices $N_G(v_i)$ that are adjacent to $v_i$, only to determine $N_G(v_i){\cap}N_G(v_j)$ as candidate cliques w and $v_k$. Next it obtains $w=w{\cap}N_G(v_k)$ by sorting $d_G(v_k)$ in the descending order. Lastly, the algorithm executes the same procedure on $G{\backslash}w$ graph to compare newly attained cliques to previously attained cliques so as to choose the lower. With this simple method, multiple independent cliques would also be attainable. When applied to various regular and irregular graphs, the algorithm proposed in this paper has obtained exact solutions to all the given graphs linear time O(n).

• Bulletin of the Korean Mathematical Society
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• v.54 no.3
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• pp.1037-1067
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• 2017

Motzkin and Straus established a close connection between the maximum clique problem and a solution (namely graph-Lagrangian) to the maximum value of a class of homogeneous quadratic multilinear functions over the standard simplex of the Euclidean space in 1965. This connection and its extensions were successfully employed in optimization to provide heuristics for the maximum clique problem in graphs. It is useful in practice if similar results hold for hypergraphs. In this paper, we develop a homogeneous multilinear function based on the structure of hypergraphs and their complement hypergraphs. Its maximum value generalizes the graph-Lagrangian. Specifically, we establish a connection between the clique number and the generalized graph-Lagrangian of 3-uniform graphs, which supports the conjecture posed in this paper.

• Bulletin of the Korean Mathematical Society
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• v.56 no.3
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• pp.569-583
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• 2019

Given a graph G, the Motzkin and Straus formulation of the maximum clique problem is the quadratic program (QP) formed from the adjacent matrix of the graph G over the standard simplex. It is well-known that the global optimum value of this QP (called Lagrangian) corresponds to the clique number of a graph. It is useful in practice if similar results hold for hypergraphs. In this paper, we attempt to explore the relationship between the Lagrangian of a hypergraph and the order of its maximum cliques when the number of edges is in a certain range. Specifically, we obtain upper bounds for the Lagrangian of a hypergraph when the number of edges is in a certain range. These results further support a conjecture introduced by Y. Peng and C. Zhao (2012) and extend a result of J. Talbot (2002). We also establish an upper bound of the clique number in terms of Lagrangians for hypergraphs.

• Korean Journal of Mathematics
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• v.28 no.3
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• pp.405-419
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• 2020

The zeroth-order general Randić index ⁰R_α(G) of the graph G is defined as ∑_u∈V(G)d(u)^α, where d(u) is the degree of vertex u and α is an arbitrary real number. In this paper, the maximum value of zeroth-order general Randić index on the graphs of order n with a given clique number is presented for any α ≠ 0, 1 and α ∉ (2, 2n-1], where n = |V (G)|. The minimum value of zeroth-order general Randić index on the graphs with a given clique number is also obtained for any α ≠ 0, 1. Furthermore, the corresponding extremal graphs are characterized.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.228-233
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• 2005

The protein side-chain packing problem (SCPP) is known to be NP-complete. Various graph theoretic based side-chain packing algorithms have been proposed. However as the size of the protein becomes larger, the sampling space increases exponentially. Hence, one approach to cope with the time complexity is to decompose the graph of the protein into smaller subgraphs. Some existing approaches decompose the graph into biconnected components at an articulation point (resulting in an at-most 21-residue subgraph) or solve the SCPP by tree decomposition (4-, 5-residue subgraph). In this regard, we had also presented a deterministic based approach called as SPWCQ using the notion of maximum edge weight clique in which we reduce SCPP to a graph and then obtain the maximum edge-weight clique of the obtained graph. This algorithm performs well for a protein of less than 500 residues. However, it fails to produce a feasible solution for larger proteins because of the size of the search space. In this paper, we present a new heuristic approach for the side-chain packing problem based on the maximum edge-weight clique finding algorithm that enables us to compute the side-chain packing of much larger proteins. Our new approach can compute side-chain packing of a protein of 874 residues with an RMSD of 1.423${\AA}$.

• Korean Management Science Review
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• v.10 no.1
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• pp.171-192
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• 1993

In this paper, a rule for dispatching operations, named the Most Dissimilar Resources (MDR) dispatching rule is presented. The MDR dispatching rule has been designed to maximize utilization of resources in a resource-constrained job shop with general precedence constraints. In shown that solving the above scheduling problem with the MDR dispatching rule is equivalent to multiple solving of the maximum clique problem. A graph theoretic approach is used to model the latter problem. The pairwise counting heuristic of computational time complexity O(n$^{2}$) is developed to solve the maximum clique problem. An attempt is made to combine the MDR dispatching rule with the existing look-ahead dispatching rules. Computational experience indicates that the combined MDR dispatching rules provide solutions of better quality and consistency than the dispatching rules tested in a resource-constrained job shop.

• ETRI Journal
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• v.37 no.5
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• pp.888-897
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• 2015

Group scheduling is an operation whereby control packets arriving in a time slot schedule their bursts simultaneously. Normally, those bursts that are of the same wavelength are scheduled on the same channel. In cases where the support of full wavelength converters is available, such scheduling can be performed on multiple channels for those bursts that are of an arbitrary wavelength. This paper presents a new algorithm for group scheduling on multiple channels. In our approach, to reach a near-optimal schedule, a maximum-weight clique needs to be determined; thus, we propose an additional algorithm for this purpose. Analysis and simulation results indicate that an optimal schedule is almost attainable, while the complexity of computation and that of implementation are reduced.

• Journal of the Korean Mathematical Society
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• v.54 no.3
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• pp.847-865
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• 2017

For a finite or an infinite set X, let $2^X$ be the power set of X. A class of simple graph, called strong Boolean graph, is defined on the vertex set $2^X{\setminus}\{X,{\emptyset}\}$, with M adjacent to N if $M{\cap}N={\emptyset}$. In this paper, we characterize the annihilating-ideal graphs $\mathbb{AG}(R)$ that are blow-ups of strong Boolean graphs, complemented graphs and preatomic graphs respectively. In particular, for a commutative ring R such that AG(R) has a maximum clique S with $3{\leq}{\mid}V(S){\mid}{\leq}{\infty}$, we prove that $\mathbb{AG}(R)$ is a blow-up of a strong Boolean graph if and only if it is a complemented graph, if and only if R is a reduced ring. If assume further that R is decomposable, then we prove that $\mathbb{AG}(R)$ is a blow-up of a strong Boolean graph if and only if it is a blow-up of a pre-atomic graph. We also study the clique number and chromatic number of the graph $\mathbb{AG}(R)$.