Two methods for the generation of chordal graphs

In this paper two methods for automatic generation of connected chordal graphs are proposed: the first one is based on results concerning the dynamic maintainance of chordality under edge insertions; the second is based on expansion/merging of maximal cliques. In both methods, chordality is preserved along the whole generation process

Generation of random chordal graphs using subtrees of a tree

Chordal graphs form one of the most studied graph classes. Several graph problems that are NP-hard in general become solvable in polynomial time on chordal graphs, whereas many others remain NP-hard. For a large group of problems among the latter, approximation algorithms, parameterized algorithms, and algorithms with moderately exponential or sub-exponential running time have been designed. Chordal graphs have also gained increasing interest during the recent years in the area of enumeration algorithms. Being able to test these algorithms on instances of chordal graphs is crucial for understanding the concepts of tractability of hard problems on graph classes. Unfortunately, only few published papers give algorithms for generating chordal graphs. Even in these papers, only very few methods aim for generating a large variety of chordal graphs. Surprisingly, none of these methods is directly based on the “intersection of subtrees of a tree” characterization of chordal graphs. In this paper, we give an algorithm for generating chordal graphs, based on the characterization that a graph is chordal if and only if it is the intersection graph of subtrees of a tree. Upon generating a random host tree, we give and test various methods that generate subtrees of the host tree. We compare our methods to one another and to existing ones for generating chordal graphs. Our experiments show that one of our methods is able to generate the largest variety of chordal graphs in terms of maximal clique sizes. Moreover, two of our subtree generation methods result in an overall complexity of our generation algorithm that is the best possible time complexity for a method generating the entire node set of subtrees in an “intersection of subtrees of a tree” representation. The instances corresponding to the results presented in this paper, and also a set of relatively small-sized instances are made available online.publishedVersio

Weakly Chordal Graphs: An Experimental Study

Graph theory is an important field that enables one to get general ideas about graphs and their properties. There are many situations (such as generating all linear layouts of weakly chordal graphs) where we want to generate instances to test algorithms for weakly chordal graphs. In my thesis, we address the algorithmic problem of generating weakly chordal graphs. A graph G=(V, E), where V is its vertices and E is its edges, is called a weakly chordal graph, if neither G nor its complement G\u27, contains an induced chordless cycle on five or more vertices. Our work is in two parts. In the first part, we carry out a comparative study of two existing algorithms for generating weakly chordal graphs. The first algorithm for generating weakly chordal graphs repeatedly finds a two-pair and adds an edge between them. The second-generation algorithm starts by constructing a tree and then generates an orthogonal layout (also weakly chordal graph) based on this tree. Edges are then inserted into this orthogonal layout until there are $m$ edges. The output graphs from these two methods are compared with respect to several parameters like the number of four cycles, run times, chromatic number, the number of non-two-pairs in the graphs generated by the second method. In the second part, we propose an algorithm for generating weakly chordal graphs by edge deletions starting from an arbitrary input random graph. The algorithm starts with an arbitrary graph to be able to generate a weakly chordal graph by the basis of edge deletion. The algorithm iterates by maintaining weak chordality by preventing any hole or antihole configurations being formed for any successful deletion of an edge

Sparse Inverse Covariance Estimation for Chordal Structures

In this paper, we consider the Graphical Lasso (GL), a popular optimization problem for learning the sparse representations of high-dimensional datasets, which is well-known to be computationally expensive for large-scale problems. Recently, we have shown that the sparsity pattern of the optimal solution of GL is equivalent to the one obtained from simply thresholding the sample covariance matrix, for sparse graphs under different conditions. We have also derived a closed-form solution that is optimal when the thresholded sample covariance matrix has an acyclic structure. As a major generalization of the previous result, in this paper we derive a closed-form solution for the GL for graphs with chordal structures. We show that the GL and thresholding equivalence conditions can significantly be simplified and are expected to hold for high-dimensional problems if the thresholded sample covariance matrix has a chordal structure. We then show that the GL and thresholding equivalence is enough to reduce the GL to a maximum determinant matrix completion problem and drive a recursive closed-form solution for the GL when the thresholded sample covariance matrix has a chordal structure. For large-scale problems with up to 450 million variables, the proposed method can solve the GL problem in less than 2 minutes, while the state-of-the-art methods converge in more than 2 hours

Equivalent relaxations of optimal power flow

Several convex relaxations of the optimal power flow (OPF) problem have recently been developed using both bus injection models and branch flow models. In this paper, we prove relations among three convex relaxations: a semidefinite relaxation that computes a full matrix, a chordal relaxation based on a chordal extension of the network graph, and a second-order cone relaxation that computes the smallest partial matrix. We prove a bijection between the feasible sets of the OPF in the bus injection model and the branch flow model, establishing the equivalence of these two models and their second-order cone relaxations. Our results imply that, for radial networks, all these relaxations are equivalent and one should always solve the second-order cone relaxation. For mesh networks, the semidefinite relaxation is tighter than the second-order cone relaxation but requires a heavier computational effort, and the chordal relaxation strikes a good balance. Simulations are used to illustrate these results.Comment: 12 pages, 7 figure