Journal of Algorithms and Computation
Volume:49 Issue: 1, Jun 2017

We solve the design spectrum problem for all theta graphs with 10, 11, 12, 13, 14 and 15 edges.

In this paper we introduce remainder cordial labeling of graphs. Let G be a (p,q) graph. Let f:V(G)rightarrow {1,2,...,p} be a 1-1 map. For each edge uv assign the label r where r is the remainder when f(u) is divided by f(v) or f(v) is divided by f(u) according as f(u)geq f(v) or f(v)geq f(u). The functionf is called a remainder cordial labeling of G if left| e_{f}(0) - e_f(1) right|leq 1 where e_{f}(0) and e_{f}(1) respectively denote the number of edges labelled with even integers and odd integers. A graph G with a remainder cordial labeling is called a remainder cordial graph. We investigate the remainder cordial behavior of path, cycle, star, bistar, crown, comb, wheel, complete bipartite K_{2,n} graph. Finally we propose a conjecture on complete graph K_{n}.the formula is not displayed correctly!

The notion of Asteroidal triples was introduced by Lekkerkerker and Boland [6]. D.G.Corneil and others [2], Ekkehard Kohler [3] further investigated asteroidal triples. Walter generalized the concept of asteroidal triples to asteroidal sets [8]. Further study was carried out by Haiko Muller [4]. In this paper we find asteroidal numbers for Direct product of cycles, Direct product of path and cycle, Strong product of paths and cycles and some more graphs.

Numerous networks as, for example, road networks, electrical networks and communication networks can be modeled by a graph. Many attempts have been made to determine how well such a network is "connected" or stated differently how much effort is required to break down communication in the system between at least some nodes. Two well-known measures that indicate how "reliable" a graph is are the "Tenacity" and "Edge-tenacity" of a graph. In this paper we present results on the tenacity and edge-tenacity, $T_e(G)$, a new invariant, for several classes of graphs. Basic properties and some bounds for edge-tenacity, $T_e(G)$, are developed. Edge-tenacity values for various classes of graphs are calculated and future work andconcluding remarks are summarized

In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated where the feasible region is formed as the intersection of two inequality fuzzy systems and Yager family of t-norms is considered as fuzzy composition. Yager family of t-norms is a parametric family of continuous nilpotent t-norms which is also one of the most frequently applied one. This family of t-norms is strictly increasing in its parameter and covers the whole spectrum of t-norms when the parameter is changed from zero to infinity. The resolution of the feasible region of the problem is firstly investigated when it is defined with max-Yager composition. Based on some theoretical results, conditions are derived for determining the feasibility. Moreover, in order to simplify the problem, some procedures are presented. It is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. A method is proposed to generate random feasible max-Yager fuzzy relational inequalities and an algorithm is presented to solve the problem. Finally, an example is described to illustrate these algorithms

Conceptually graph vulnerability relates to the study of graphintactness when some of its elements are removed. The motivation forstudying vulnerability measures is derived from design and analysisof networks under hostile environment. Graph tenacity has been anactive area of research since the the concept was introduced in1992. The tenacity T(G) of a graph G is defined asbegin{center} T(G)=displaystyle min_{Asubset V(G)}{frac{mid Amid  +tau(G-A)}{omega(G-A)}}end{center}where tau(G-A) denotes the order (the number of vertices) of alargest component of G-A and omega(G-A) is the number ofcomponents of G-A. In this paper we discuss tenacity and its properties invulnerability calculation.the formula is not displayed correctly!

It is well-known that, given inorder traversal along with one of the preorder or postorder traversals of a binary tree, the tree can be determined uniquely. Several algorithms have been proposed to reconstruct a binary tree from its inorder and preorder traversals. There is one study to reconstruct a binary tree from its inorder and postorder traversals, and this algorithm takes running time of  BigO{emph{n}^2} . In this paper, we present proc{InPos} an improved algorithm to reconstruct a binary tree from its inorder and postorder traversals. The running time and space complexity of the algorithm are an order of BigTheta{emph{n}} and BigTheta{emph{n}} respectively, which we prove to be optimal.  The proc{InPos} algorithm not only reconstructs the binary tree, but also it determines different types of the nodes in a binary tree; nodes with two children, nodes with one child, and nodes with no child. At the end, the proc{InPos} returns a matrix-based structure to represent the binary tree, and enabling  access to any structural information of the reconstructed tree in linear time with any given tree traversals.the formula is not displayed correctly!

In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated where the feasible region is formed as the intersection of two inequality fuzzy systems and Hamacher family of t-norms is considered as fuzzy composition. Hamacher family of t-norms is a parametric family of continuous strict t-norms, whose members are decreasing functions of the parameter. The resolution of the feasible region of the problem is firstly investigated when it is defined with max-Hamacher composition. Based on some theoretical results, a necessary and sufficient condition and three other necessary conditions are derived for determining the feasibility. Moreover, in order to simplify the problem, some procedures are presented. It is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. A method is proposed to generate random feasible max-Hamacher fuzzy relational inequalities and an algorithm is presented to solve the problem. Finally, an example is described to illustrate these algorithms.