204988011
A class of additive multiplicative graph functions
For a fixed graph G, the capacity function for G, Po, is defined by Pc(H)= lim,__,®[yo(H')] TM, where y6(H) is the maximum number of disjoint G's in H. In [2], Hsu proved that Pr2 can be viewed as a lower bound for multiplicative increasing graph functions. But it was not known whether Pr2 is multiplicative or not. In this paper, we prove that Pc is multiplicative and additive for some graphs G which include K2. Some properties of Pc are also discussed in this paper.
16940810
Vertex and Tree Arboricities of Graphs
This paper studies the following variations of arboricity of graphs. The vertex (respectively, tree) arboricity of a graph G is the minimum number va(G) (respectively, ta(G)) of subsets into which the vertices of G can be partitioned so that each subset induces a forest (respectively, tree). This paper studies the vertex and the tree arboricities on various classes of graphs for exact values, algorithms, bounds, hamiltonicity and NP-completeness. The graphs investigated in this paper include block-cactus graphs, series-parallel graphs, cographs and planar graphs.
198001
Proper interval graphs and the guard problem
This paper is a study of the hamiltonicity of proper interval graphs with applications to the guard problem in spiral polygons. We prove that proper interval graphs with ?2 vertices have hamiltonian paths, those with ?3 vertices have hamiltonian cycles, and those with ?4 vertices are hamiltonian-connected if and only if they are, respectively, 1-, 2-, or 3-connected. We also study the guard problem in spiral polygons by connecting the class of nontrivial connected proper interval graphs with the class of stick-intersection graphs of spiral polygons.
18457324
On the higher-order edge toughness of a graph
For an integer c, 1?c??V(G)??1, we define the cth-order edge toughness of a graph G as tc(G)=min|X|?(G?X)?cX?E(G) & ?(G?X)>c The objective of this paper is to study this generalized concept of edge toughness. Besides giving the of the cth-order edge toughness ?c(G) of a graph G, we prove that `?c(G)?k if and only if G has k edge-disjoint spanning forests with exactly c components?. We also study the `balancity? of a graph G of order p and size q, which is defined as the smallest positive integer c such that ?c(G) = p/(p?c).
205000421
A Simple Approach to Implementing Multiplication with Small Tables
Abstract A new approach for implementing multiplication with a small table is presented. This approach is much simpler and thus easier to be implemented.
27005423
Binding number and toughness for matching extension
Abstract A graph G of even order is said to be k -extendable if every matching of size k in G can be extended to a 1-factor of G . Plummer (1988) showed that a graph G is k -extendable if tough ( G ) > k , and we here prove that G is also k -extendable if bind ( G ) > max {k, (7k + 13)/12}.
2867815
Matchings and matching extensions in graphs
Abstract Let G be a graph with a perfect matching and k be an integer such that 1 ⩽ k vbV ( G ) vb /2. Then G is said to be k -extendable if every matching of size k in G extends to a perfect matching of G . Plummer (1994) proved that every (2 k + 1)-connected K 1,3 -free graph of even order is k -extendable. In this paper, it was proved that every (2 k + n − 2)-connected K 1, n -free graph of even order is k -extendable. Also, an answer to the problem of characterizing maximal k -extendable graphs posted by Plummer (1994) and Saito (1989/90) is given. Besides, we show that a regular graph of even order belongs to the first class if its any two odd cycles have at least a vertex in common.
21349955
A unified optimization framework for microelectronics industry
In this paper, an object-oriented unified optimization framework (UOF) for general problem optimization is proposed. Based on evolutionary algorithms, numerical deterministic methods, and C++ objective design, the UOF possesses real-world applications for various optimization problems. The UOF is with interface between defining a general problem and generic solver; hence the two areas are bridged. The UOF's components are categorized into problem and solver parts, and they work independently. Therefore, the reusable high level code allows the adaptation to new problem and solver quickly. Practical applications in microelectronics industry confirm the functionality of the developed unified optimization framework.