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An Introduction to Chordal Graphs and Clique Trees

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TLDR
In this paper, a unified and elementary introduction to the standard characterizations of chordal graphs and clique trees is presented, as well as a detailed proof of all the results.
Abstract
Clique trees and chordal graphs have carved out a niche for themselves in recent work on sparse matrix algorithms, due primarily to research questions associated with advanced computer architectures. This paper is a unified and elementary introduction to the standard characterizations of chordal graphs and clique trees. The pace is leisurely, as detailed proofs of all results are included. We also briefly discuss applications of chordal graphs and clique trees in sparse matrix computations.

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Journal ArticleDOI

iSAM2: Incremental smoothing and mapping using the Bayes tree

TL;DR: The Bayes tree is applied to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps.
Journal ArticleDOI

Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing

TL;DR: The theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem, are presented, and both simulation results and actual SLAM experiments are presented that underscore the potential of these methods as an alternative to EKF-based approaches.
Book ChapterDOI

Sums of Squares, Moment Matrices and Optimization Over Polynomials

TL;DR: This work considers the problem of minimizing a polynomial over a semialgebraic set defined byPolynomial equations and inequalities, which is NP-hard in general and reviews the mathematical tools underlying these properties.
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Learning equivalence classes of bayesian-network structures

TL;DR: In this paper, the authors consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging, and show that more sophisticated search algorithms are likely to benefit much more.
Journal ArticleDOI

Sums of Squares and Semidefinite Program Relaxations for Polynomial Optimization Problems with Structured Sparsity

TL;DR: Using a correlative sparsity pattern graph, sets of the supports for sums of squares polynomials that lead to efficient SOS and semidefinite program (SDP) relaxations are obtained.
References
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Journal ArticleDOI

Shortest connection networks and some generalizations

TL;DR: In this paper, the basic problem of interconnecting a given set of terminals with a shortest possible network of direct links is considered, and a set of simple and practical procedures are given for solving this problem both graphically and computationally.
Book

Algorithmic graph theory and perfect graphs

TL;DR: This new Annals edition continues to convey the message that intersection graph models are a necessary and important tool for solving real-world problems and remains a stepping stone from which the reader may embark on one of many fascinating research trails.
Book

Data Structures and Network Algorithms

TL;DR: This paper presents a meta-trees tree model that automates the very labor-intensive and therefore time-heavy and therefore expensive process of manually selecting trees to grow in a graph.
Journal ArticleDOI

Incidence matrices and interval graphs

TL;DR: In this article, the problem of determining when a graph is an interval graph is a special case of the following problem concerning (0, 1)-matrices: when can the rows of such a matrix be permuted so as to make the 1's in each colum appear consecutively.