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2018, Journal of Global Optimization
Distance Geometry (DG) is based on distances rather than points and angles. The fundamental problem of DG is the Distance Geometry Problem (DGP), which is an inverse problem to "given a set of points in R K , compute some of the pairwise distances". More precisely, given an integer K > 0 and a simple undirected graph G = (V , E) with a non-negative weight function d : E → R + defined on the edges, it asks whether there exists a realization x : V → R K such that ∀{i, j} ∈ E x i − x j = d i j. (1) DIMACS generously supported the Distance Geometry Theory and Applications workshop to which this special issue is dedicated. NSF support (through DIMACS) is also gratefully acknowledged.
ArXiv, 2021
The Distance Geometry Problem asks for a realization of a given weighted graph in R . Two variants of this problem, both originating from protein conformation, are based on a given vertex order (which abstracts the protein backbone). Both variants involve an element of discrete decision in the realization of the next vertex in the order using K preceding (already realized) vertices. The difference between these variants is that one requires the K preceding vertices to be contiguous. The presence of this constraint allows one to prove, via a combinatorial counting of the number of solutions, that the realization algorithm is fixed-parameter tractable. Its absence, on the other hand, makes it possible to efficiently construct the vertex order directly from the graph. Deriving a combinatorial counting method without using the contiguity requirement would therefore be desirable. In this paper we prove that, unfortunately, such a counting method cannot be devised in general.
Distance Geometry, 2012
Given a weighted undirected graph G = (V, E, d) with d : E → Q + and a positive integer K, the Distance Geometry Problem (DGP) asks to find an embedding x : V → R K of G such that for each edge {i, j} we have x i − x j = d i j. Saxe proved in 1979 that the DGP is NP-complete with K = 1 and doubted the applicability of the Turing machine model to the case with K > 1, because the certificates for YES instances might involve real numbers. This chapter is an account of an unfortunately failed attempt to prove that the DGP is in NP for K = 2. We hope that our failure will motivate further work on the question.
2020
The distance geometry problem consists in finding a realization of a weighed graph in a Euclidean space of given dimension, where the edges are realized as straight segments of length equal to the edge weight. We propose and test a new mathematical programming formulation based on the incidence between cycles and edges in the given graph.
2019
Stimulated by the famous plane coloring problem Eggleton coined the term distance graph and studied widely the prime distance graphs. A prime distance graph (PDG) G(Z,D) is one whose vertex set V is the set of integers Z and the distance set D is a subset of the set of primes P . The edge set of G denoted E is the one whose elements (u, v) for any u, v ∈ V (G) are characterized by the property that d(u, v) ∈ D where d(u, v) = |u − v| . According to J.D.Laison, C. Starr and A. Walker a graph G is a PDG if there exists a 1-1 labeling f : V (G)→ Z such that for any two adjacent vertices u and v the integer |f(u)− f(v)| is a prime. Further they called such a labelling of V (G) a prime distance labelling (PDL) of G . In this paper we prove certain existence and non-existence results concerning PDG and PDL and study the relationship between them. We also discuss certain applications besides raising some open problems.
Surveys on Discrete and Computational Geometry, 2008
The article surveys structural characterizations of several graph classes defined by distance properties, which have in part a general algebraic flavor and can be interpreted as subdirect decomposition. The graphs we feature in the first place are the median graphs and their various kinds of generalizations, e.g., weakly modular graphs, or fiber-complemented graphs, or l 1-graphs. Several kinds of l 1-graphs admit natural geometric realizations as polyhedral complexes. Particular instances of these graphs also occur in other geometric contexts, for example, as dual polar graphs, basis graphs of (even ∆-)matroids, tope graphs, lopsided sets, or plane graphs with vertex degrees and face sizes bounded from below. Several other classes of graphs, e.g., Helly graphs (as injective objects), or bridged graphs (generalizing chordal graphs), or tree-like graphs such as distance-hereditary graphs occur in the investigation of graphs satisfying some basic properties of the distance function, such as the Helly property for balls, or the convexity of balls or of the neighborhoods of convex sets, etc. Operators between graphs or complexes relate some of the graph classes reported in this survey.
Discrete & Computational Geometry, 1994
Given an undirected edge-weighted graph G = (V, E), a subgraph G' = (IT, E') is a t-spanner of G if, for every u, v ~ V, the weighted distance between u and v in G' is at most t times the weighted distance between u and v in G. We consider the problem of approximating the distances among points of a Euclidean metric space: given a finite set V of points in ~a, we want to construct a sparse t-spanner of the complete weighted graph induced by V. The weight of an edge in these graphs is the Euclidean distance between the endpoints of the edge. We show by a simple greedy argument that, for any t > 1 and any V c R a, a t-spanner G of V exists such that G has degree bounded by a function of d and r The analysis of our bounded degree spanners improves over previously known upper bounds on the minimum number of edges of Euclidean t-spanners, even compared with spanners of bounded average degree. Our results answer two open problems, one proposed by Vaidya and the other by Keil and Gutwin. The main result of the paper concerns the case of dimension d = 2. It is fairly easy to see that, for some t (t > 7.6), t-spanners of maximum degree 6 exist for any set of points in the Euclidean plane, but it was not known that degree 5 would suffice. We prove that for some (fixed) t, t-spanners of degree 5 exist for any set of points in the plane. We do not know if 5 is the best possible upper bound on the degree. * This research was supported by Conselho Nacional de Desenvolvimento Cientifico e Tecnol6gico, Proc 203039/87.4 (Brazil). 214 J. Soares shortest path between x and y. We say that a subgraph G' = (V, E') (with the same weights on E') is a t-spanner of G if, for every x, y 6 V, dG,(x, y) < t" da(x, y). The number t is a measure of how well G' approximates G with respect to the distances. The construction of t-spanners has received recent attention in several works: [2], [3], [5], [8], [9], [11], and [18], among others. Given a set V ~ •a the complete Euclidean graph on V is the complete graph on V where each edge weight is the Euclidean distance I[x-Y]I. In this paper we consider the problem of constructing bounded degree spanners of complete Euclidean graphs. For brevity we write t-spanner of V instead of t-spanner of the complete Euclidean graph on V. Let A(G) denote the maximum degree of a graph G. Dobkin et al. [5] mention that Feder and others had shown that, for some fixed t and for any set V of points in the Euclidean plane, a t-spanner G of V exists such that A(G) < 7. Then they ask what would be the minimum A for which such a result is possible? This paper has a partial answer to this question. Our main result (Section 4) is that, for some fixed t, t-spanners with A < 5 exist. Nisan [10] has proved the same for A < 6. Section 2 contains the basic algorithm used to construct bounded degree t-spanners. Although the algorithm has been used before by Althrfer et al. [1] and Soares [16] to construct t-spanners for arbitrary graphs, it was not known that the algorithm also constructs bounded degree spanners for complete Euclidean graphs. Section 3 contains a brief analysis of the problem when V is in d-dimensional Euclidean space. We show that, for any t > 1 and any V c ~d, a t-spanner G of V exists where A(G) is bounded by a function that depends only on d and t. This answers a question proposed by Keil and Gutwin in [8]. This bound on the maximum degree implies an improvement on the previously known upper bounds on the number of edges sufficient to build Euclidean spanners. Then we show that, for each dimension d, the least A(G) for which our algorithm constructs Od(1)spanners coincides with the kissing number in dimension d. (Od(1) denotes some function of d, i.e., a constant for each d.) Section 4 contains our main result, the construction of O(1)-spanners of degree 5 for any set of points in the Euclidean plane.
Discrete Applied Mathematics, 2011
A new class of distances for graph vertices is proposed. This class contains parametric families of distances which reduce to the shortest-path, weighted shortestpath, and the resistance distances at the limiting values of the family parameters. The main property of the class is that all distances it comprises are graph-geodetic: d(i, j) + d(j, k) = d(i, k) if and only if every path from i to k passes through j. The construction of the class is based on the matrix forest theorem and the transition inequality.
Discrete Applied Mathematics, 1991
Maehara, H., Distances in a rigid unit-distance graph in the plane, Discrete Applied Mathematics 31 (1991) 193-200. It is proved that a number d > 0 can appear as the Euclidean distance between two vertices in some "rigid" unit-distance graph in the plane if and only if d is an algebraic number.
Lecture Notes in Computer Science, 2017
We introduce the dynamical distance geometry problem (dynDGP), where vertices of a given simple weighted undirected graph are to be embedded at different times t. Solutions to the dynDGP can be seen as motions of a given set of objects. In this work, we focus our attention on a class of instances where motion inter-frame distances are not available, and reduce the problem of embedding every motion frame as a static distance geometry problem. Some preliminary computational experiments are presented.
Digital distance geometry (DDG) is the study of distances in the geometry of digitized spaces. This was introduced approximately 25 years ago, when the study of digital geometry itself began, for providing a theoretical background to digital picture processing algorithms. In this survey we focus our attention on the DDG of arbitrary dimensions and other related issues and compile an up-to-date list of references on the topic.
Springer Optimization and Its Applications, 2018
Distance Geometry is based on the inverse problem that asks to find the positions of points, in a Euclidean space of given dimension, that are compatible with a given set of distances. We briefly introduce the field, and discuss some open and promising research areas.
Optimization Letters, 2012
We introduce the Discretizable Distance Geometry Problem in R 3 (DDGP 3), which consists in a subclass of instances of the Distance Geometry Problem for which an embedding in R 3 can be found by means of a discrete search. We show that the DDGP 3 is a generalization of the Discretizable Molecular Distance Geometry Problem (DMDGP), and we discuss the main differences between the two problems. We prove that the DDGP 3 is NP-hard and we extend the Branch & Prune (BP) algorithm, previously used for the DMDGP, for solving instances of the DDGP 3. Protein graphs may or may not be in DMDGP and/or DDGP 3 depending on vertex orders and edge density. We show experimentally that as distance thresholds decrease, PDB protein graphs which fail to be in the DMDGP still belong to DDGP 3 , which means that they can still be solved using a discrete search.
2020
We propose a new algorithm for Discretizable Molecular Distance Geometry problems (DMDGPs), a class of Distance Geometry problems (DGPs) whose search space can be discretized and represented by a binary tree. By efficiently exploiting the many interesting symmetry properties of DMDGP instances, the new algorithm solves a sequence of nested and overlapped DMDGP subproblems rather than exploring the binary tree in a depth first manner as the classic Branch-and-Prune (BP) algorithm. Computational results on artificially generated instances show that the new algorithm outperforms the classic BP algorithm in sparse DMDGPs.
Algorithmica
The fundamental inverse problem in distance geometry is the one of finding positions from interpoint distances. The Discretizable Molecular Distance Geometry Problem (DMDGP) is a subclass of the Distance Geometry Problem (DGP) whose search space can be discretized and represented by a binary tree, which can be explored by a Branch-and-Prune (BP) algorithm. It turns out that this combinatorial search space possesses many interesting symmetry properties that were studied in the last decade. In this paper, we present a new algorithm for this subclass of the DGP, which exploits DMDGP symmetries more effectively than its predecessors. Computational results show that the speedup, with respect to the classic BP algorithm, is considerable for sparse DMDGP instances related to protein conformation.
Optimization Letters
The Distance Geometry Problem (DGP) is the problem of determining whether a realization for a simple weighted undirected graph G = (V, E, d) in a given Euclidean space exists so that the distances between pairs of realized vertices u, v ∈ V correspond to the weights d uv , for each {u, v} ∈ E. We focus on a special class of DGP instances, referred to as the Discretizable DGP (DDGP), and we introduce the K-discretization and the K-incident graphs for the DDGP class. The K-discretization graph is independent on the vertex order that can be assigned to V , and can be useful for discovering whether one of such orders actually exists so that the DDGP assumptions are satisfied. The use of a given vertex order allows the definition of another important graph, the K-incident graph, which is potentially useful for performing pre-processing analysis on the solution set of DDGP instances.
SIAM Review, 2014
Euclidean distance geometry is the study of Euclidean geometry based on the concept of distance. This is useful in several applications where the input data consists of an incomplete set of distances, and the output is a set of points in Euclidean space that realizes the given distances. We survey some of the theory of Euclidean distance geometry and some of the most important applications: molecular conformation, localization of sensor networks and statics.
TOP
Data are often represented as graphs. Many common tasks in data science are based on distances between entities. While some data science methodologies natively take graphs as their input, there are many more that take their input in vectorial form. In this survey, we discuss the fundamental problem of mapping graphs to vectors, and its relation with mathematical programming. We discuss applications, solution methods, dimensional reduction techniques, and some of their limits. We then present an application of some of these ideas to neural networks, showing that distance geometry techniques can give competitive performance with respect to more traditional graph-to-vector mappings.
Beiträge Zur Algebra Und Geometrie / Contributions To Algebra And Geometry, 2022
The concept of n-distance was recently introduced to generalize the classical definition of distance to functions of n arguments. In this paper we investigate this concept through a number of examples based on certain geometrical constructions. In particular, our study shows to which extent the computation of the best constant associated with an n-distance may sometimes be difficult and tricky. It also reveals that two important graph theoretical concepts, namely the total length of the Euclidean Steiner tree and the total length of the minimal spanning tree constructed on n points, are instances of n-distances.
2014
We propose a set of formulations and reformulations of the Distance Geometry Problem, which we evaluate with both local and global off-the-shelf solvers. The local solvers are cast in a global optimization metaheuristic (Variable Neighbourhood Search) since the problem is nonconvex and non-global optima are usually of limited practical interest.
2013
By a graph G = (V,E), we mean a finite undirected graph with neither loops nor multiple edges. The order and size of G are denoted by n = |V | and m = |E| respectively. For graph theoretic terminology we refer to Chartrand and Lesniak [7]. In Chapter 1, we collect some basic definitions and theorems on graphs which are needed for the subsequent chapters. The distance d(u, v) between two vertices u and v of a connected graph G is the length of a shortest u-v path in G. There are several distance related concepts and parameters such as eccentricity, radius, diameter, convexity and metric dimension which have been investigated by several authors in terms of theory and applications. An excellent treatment of various distances and distance related parameters are given in Buckley and Harary [6]. Let G = (V,E) be a graph. Let v ∈ V . The open neighborhood N(v) of a vertex v is the set of vertices adjacent to v. Thus N(v) = {w ∈ V : wv ∈ E}. The closed neighborhood of a vertex v, is the set...
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