We present explicit and parametric forms of transformation matrices for three well-known and widely used symmetry groups: S 2 , C 2v and C 4v . Group representation theory is the most powerful method for exploiting symmetry. We propose an efficient algorithm for systematic generation of reducible representations that can be combined linearly to obtain the projection operators. The exact column spaces of these projection operators are calculated and integrated through special orderings, leading to exact explicit and parametric forms of transformation matrices. The transformation matrices could be used directly for block diagonalization of single-variable scalar field problems. Another algorithm is proposed to extend the application of the method to nonscalar and multivariable field problems. Finally, the generality and efficiency of the proposed method in relation to computation times and the accuracy of results are illustrated through examples from spectral decomposition, free vibration, buckling of FEMs and boundary element analysis of a symmetric field. One of the earliest applications of group representation theory to the stability of structures was developed by Renton [5]. The theory has since been used extensively for the study of local and global bifurcation analyses. Among the outstanding studies in bifurcation analysis are Sattinger [6], Golubitsky and Schaeffer [7,8], Ikeda and Murota [9, 10], Ikeda et al. [11,12], Healey [13] and Wohlever and Healey [14]. Group representation theory has also been employed effectively in the vibration analysis of symmetric structures by Healey and Treacy [15], Zlokovic [16], Zingoni [17, 18] and Mohan and Pratap [19]. Eigenvalue problems related to the vibration of massspring systems were studied by Kaveh and Nikbakht [20], Kaveh and Jahanmohammadi [21] and Zingoni [22] using group theory. Zingoni [23,24] used group theory to simplify the generation of Node adjacency matrix: Let G be a graph with N nodes. The adjacency matrix A is an N N matrix in which the entry in row i and column j is 1 if node i is adjacent toj , and is 0 otherwise. Note that, for simple graphs (graphs without loops and multiple edges) the diagonal entries of the adjacency matrix are always zero; however, the existence of k loops at node i implies that node i is connected to itself by k edges. In this case, the i-th diagonal entry in the adjacency matrix should equal k.Laplacian matrix: The Laplacian matrix of graph G (denoted by L) is defined as
Symmetry subspacesBy defining a coordinate system for a configuration (the configuration could be a graph, a structural model, etc.) we can represent all symmetry operations as matrices. These matrices will change according to different forms of coordinate systems. This form of representation is called the reducible matrix representation and denoted here by P(see [29] for more details). However, there is always a special coordinate system (a symmetry-adapted coordinate system) such that each symmetry operation is represented on it as a unique matrix. These f...