On the maximal number of real embeddings of minimally rigid graphs in R2, R3 and S2. (January 2021)

• Record Type: Journal Article
• Authors: Bartzos, Evangelos; Emiris, Ioannis Z.; Legerský, Jan; Tsigaridas, Elias
• Is Part Of: Journal of symbolic computation. Volume 102(2020)
• Page Start: 189
• Page End: 208
• Publication Date: 2021-01
• Subjects: Rigid graph -- Laman graph -- Real bound -- Coupler curve -- Cayley-Menger matrix -- Mixed volume; Mathematics -- Data processing -- Periodicals; Numerical analysis -- Data processing -- Periodicals; Automatic programming (Computer science) -- Periodicals; Mathématiques -- Informatique -- Périodiques; Analyse numérique -- Informatique -- Périodiques; Programmation automatique -- Périodiques; Automatic programming (Computer science); Mathematics -- Data processing; Numerical analysis -- Data processing; Periodicals; Electronic journals; 510.285
• DOI: 10.1016/j.jsc.2019.10.015
• Languages: English
• ISSNs: 0747-7171

Abstract

Abstract: Rigidity theory studies the properties of graphs that can have rigid embeddings in a euclidean space R d or on a sphere and other manifolds which in addition satisfy certain edge length constraints. One of the major open problems in this field is to determine lower and upper bounds on the number of realizations with respect to a given number of vertices. This problem is closely related to the classification of rigid graphs according to their maximal number of real embeddings. In this paper, we are interested in finding edge lengths that can maximize the number of real embeddings of minimally rigid graphs in the plane, space, and on the sphere. We use algebraic formulations to provide upper bounds. To find values of the parameters that lead to graphs with a large number of real realizations, possibly attaining the (algebraic) upper bounds, we use some standard heuristics and we also develop a new method inspired by coupler curves. We apply this new method to obtain embeddings in R 3 . One of its main novelties is that it allows us to sample efficiently from a larger number of parameters by selecting only a subset of them at each iteration. Our results include a full classification of the 7-vertex graphs according to their maximal numbers of real embeddings in the cases of the embeddings in R 2 and R 3, while in the case of S 2 we achieve this classification for all 6-vertex graphs. Additionally, by increasing the number of embeddings of selected graphs, we improve the previously known asymptotic lower bound on the maximum number of realizations. The methods and the results concerning the spatial embeddings are part of the proceedings of ISSAC 2018 (Bartzos et al., 2018 ).