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2014, SIAM Review
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.
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.
International Transactions in Operational Research, 2010
Distance geometry problems arise from the need to position entities in the Euclidean K-space given some of their respective distances. Entities may be atoms (molecular distance geometry), wireless sensors (sensor network localization), or abstract vertices of a graph (graph drawing). In the context of molecular distance geometry, the distances are usually known because of chemical properties and Nuclear Magnetic Resonance experiments; sensor networks can estimate their relative distance by recording the power loss during a two-way exchange; finally, when drawing graphs in 2D or 3D, the graph to be drawn is given, and therefore distances between vertices can be computed. Distance geometry problems involve a search in a continuous Euclidean space, but sometimes the problem structure helps reduce the search to a discrete set of points. In this paper we survey some continuous and discrete methods for solving some problems of molecular distance geometry.
2017
The fundamental problem of distance geometry consists in finding a realization of a given weighted graph in a Euclidean space of given dimension, in such a way that vertices are realized as points and edges as straight segments having the same lengths as their given weights. This problem arises in structural proteomics, wireless sensor networks, and clock synchronization protocols to name a few applications. The well-known Isomap method is a dimensionality reduction heuristic which projects finite but high dimensional metric spaces into the "most significant" lower dimensional ones, where significance is measured by the magnitude of the corresponding eigenvalues. We start from a simple observation, namely that Isomap can also be used to provide approximate realizations of weighted graphs very eciently, and then derive and benchmark six new heuristics.
International Transactions in Operational Research
The Distance Geometry Problem (DGP) consists in finding an embedding in a metric space of a given weighted undirected graph such that for each edge in the graph, the corresponding distance in the embedding belongs to a given distance interval. We discuss the relationship between the existence of a graph embedding in a Euclidean space and the existence of a graph embedding in a lattice. Different approaches, including two integer programming models (IP) and a constraint programming (CP) approach are presented to test feasibility of the DGP. The two IP models are improved with the inclusion of valid inequalities and the CP approach is improved with the use of an algorithm to perform a domain reduction. The main motivation to this work is to derive new pruning devices within branch and prune algorithms for instances occurring in real applications related to determination of molecular conformations, which is a particular case of the DGP. A computational study based on a set of small sized instances from molecular conformations is reported. This study compares the running times of the different approaches to check feasibility.
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.
International Transactions in Operational Research, 2015
This is a partial account of the fascinating history of Distance Geometry. We make no claim to completeness, but we do promise a dazzling display of beautiful, elementary mathematics. We prove Heron's formula, Cauchy's theorem on the rigidity of polyhedra, Cayley's generalization of Heron's formula to higher dimensions, Menger's characterization of abstract semi-metric spaces, a result of Gödel on metric spaces on the sphere, and Schoenberg's equivalence of distance and positive semidefinite matrices, which is at the basis of Multidimensional Scaling.
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.
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.
Journal of Biomolecular Nmr, 1992
In this paper, we present a reassessment of the sampling properties of the metric matrix distance geometry algorithm, which is in wide-spread use in the determination of three-dimensional structures from nuclear magnetic resonance (NMR) data. To this end, we compare the conformational space sampled by structures generated with a variety of metric matrix distance geometry protocols. As test systems we use an unconstrained polypeptide, and a small protein (rabbit neutrophil defensin peptide 5) for which only few tertiary distances had been derived from the NMR data, allowing several possible folds of the polypeptide chain. A process called ‘metrization’ in the preparation of a trial distance matrix has a very large effect on the sampling properties of the algorithm. It is shown that, depending on the metrization protocol used, metric matrix distance geometry can have very good sampling properties'indeed, both for the unconstrained model system and the NMR-structure case. We show that the sampling properties are to a great degree determined by the way in which the first few distances are chosen within their bounds. Further, we present a new protocol (‘partial metrization’) that is computationally more efficient but has the same excellent sampling properties. This novel protocol has been implemented in an expanded new release of the program X-PLOR with distance geometry capabilities.
In this paper a geometric approach is suggested to ®nd the closest approximation to Euclidean metric based on geometric measures of the digital circles in 2D and the digital spheres in 3D for the generalized octagonal distances. First we show that the vertices of the digital circles (spheres) for octagonal distances can be suitably approximated as a function of the number of neighborhood types used in the sequence. Then we use these approximate vertex formulae to compute the geometric features in an approximate way. Finally we minimize the errors of these measurements with respect to respective Euclidean discs to identify the best distances. We have veri®ed our results by experimenting with analytical error measures suggested earlier. We have also compared the performances of the good octagonal distances with good weighted distances. It has been found that the best octagonal distance in 2D (f1Y 1Y 2g) performs equally good with respect to the best one for the weighted distances (h3Y 4i). In fact in 3D, the octagonal distance f1Y 1Y 3g has an edge over the other good weighted distances. Ó
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.
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.
A generalized distance measure called m-neighbor distance in n-D quantized space is presented.
Annals of Operations Research, 2018
In the 2 years since our last 4OR review of distance geometry methods with applications to proteins and nanostructures, there has been rapid progress in treating uncertainties in the discretizable distance geometry problem; and a new class of geometry problems started to be explored, namely vector geometry problems. In this work we review this progress in the context of the earlier literature. Keywords Distance geometry • Graph rigidity • Molecular conformations • Nanostructures This is an updated version of the paper "Assigned and unassigned distance geometry: applications to biological molecules and nanostructures" that appeared in 4OR-Q J Oper Res (2016) 14: 337-376.
Electronic Notes in Discrete Mathematics, 2015
We present an efficient algorithm to find a realization of a (full) n × n squared Euclidean distance matrix in the smallest possible dimension. Most existing algorithms work in a given dimension: most of these can be transformed to an algorithm to find the minimum dimension, but gain a logarithmic factor of n in their worstcase running time. Our algorithm performs cubically in n (and linearly when the dimension is fixed, which happens in most applications).
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.
ABSTBACT An analysis of paths and distances in n dimensions is carried out using variable neigbborhood sequences. A symbolic expression for the distance function between any two points in this quantized space is derived. An algorithm for finding the shortest path is presented. The necessary and sufficient condition for such distance functions to satisfy the properties of a metric has been derived. Certain practical and efficient methods to check for metric properties are also presented.
The Journal of Chemical Physics, 2013
In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. We show that these metrics correlate well with the RMSD between two configurations if this RMSD is obtained from a global minimization over all translations, rotations and permutations of atomic indices. We introduce a Monte Carlo approach to obtain this global minimum of the RMSD between configurations.
wellbehaved, where p is the length of the sequence B. We have also reviewed the other weaker and stronger conditions of triangularity and have introduced a new weaker condition here to reformulate the strategy for deciding the metricity of a d(B).
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