11 research outputs found
Euclidean distance geometry and applications
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.Comment: 64 pages, 21 figure
Operations research: from computational biology to sensor network
In this dissertation we discuss the deployment of combinatorial optimization methods for modeling and solve real life problemS, with a particular emphasis to two biological problems arising from a common scenario: the reconstruction of the three-dimensional shape of a biological molecule from Nuclear Magnetic Resonance (NMR) data.
The fi rst topic is the 3D assignment pathway problem (APP) for a RNA molecule.
We prove that APP is NP-hard, and show a formulation of it based on edge-colored
graphs. Taking into account that interactions between consecutive nuclei in the NMR
spectrum are diff erent according to the type of residue along the RNA chain, each color
in the graph represents a type of interaction. Thus, we can represent the sequence of interactions as the problem of fi nding a longest (hamiltonian) path whose edges follow a given order of colors (i.e., the orderly colored longest path). We introduce three alternative IP formulations of APP obtained with a max flow problem on a directed graph with packing constraints over the partitions, which have been compared among themselves. Since the last two models work on cyclic graphs, for them we proposed an algorithm based on the solution of their relaxation combined with the separation of cycle inequalities in a Branch & Cut scheme.
The second topic is the discretizable distance geometry problem (DDGP), which is
a formulation on discrete search space of the well-known distance geometry problem
(DGP). The DGP consists in seeking the embedding in the space of a undirected graph, given a set of Euclidean distances between certain pairs of vertices. DGP has two important applications: (i) fi nding the three dimensional conformation of a molecule from a subset of interatomic distances, called Molecular Distance Geometry Problem, and (ii) the Sensor Network Localization Problem. We describe a Branch & Prune (BP) algorithm
tailored for this problem, and two versions of it solving the DDGP both in protein
modeling and in sensor networks localization frameworks. BP is an exact and exhaustive
combinatorial algorithm that examines all the valid embeddings of a given weighted
graph G=(V,E,d), under the hypothesis of existence of a given order on V. By
comparing the two version of BP to well-known algorithms we are able to prove the
e fficiency of BP in both contexts, provided that the order imposed on V is maintained
New error measures and methods for realizing protein graphs from distance data
The interval Distance Geometry Problem (iDGP) consists in finding a
realization in of a simple undirected graph with
nonnegative intervals assigned to the edges in such a way that, for each edge,
the Euclidean distance between the realization of the adjacent vertices is
within the edge interval bounds. In this paper, we focus on the application to
the conformation of proteins in space, which is a basic step in determining
protein function: given interval estimations of some of the inter-atomic
distances, find their shape. Among different families of methods for
accomplishing this task, we look at mathematical programming based methods,
which are well suited for dealing with intervals. The basic question we want to
answer is: what is the best such method for the problem? The most meaningful
error measure for evaluating solution quality is the coordinate root mean
square deviation. We first introduce a new error measure which addresses a
particular feature of protein backbones, i.e. many partial reflections also
yield acceptable backbones. We then present a set of new and existing quadratic
and semidefinite programming formulations of this problem, and a set of new and
existing methods for solving these formulations. Finally, we perform a
computational evaluation of all the feasible solverformulation combinations
according to new and existing error measures, finding that the best methodology
is a new heuristic method based on multiplicative weights updates
Proceedings of the XIII Global Optimization Workshop: GOW'16
[Excerpt] Preface: Past Global Optimization Workshop shave been held in Sopron (1985 and 1990), Szeged (WGO, 1995), Florence (GOâ99, 1999), Hanmer Springs (Letâs GO, 2001), Santorini (Frontiers in GO, 2003), San JosĂ© (Goâ05, 2005), Mykonos (AGOâ07, 2007), Skukuza (SAGOâ08, 2008), Toulouse (TOGOâ10, 2010), Natal (NAGOâ12, 2012) and MĂĄlaga (MAGOâ14, 2014) with the aim of stimulating discussion between senior and junior researchers on the topic of Global Optimization. In 2016, the XIII Global Optimization Workshop (GOWâ16) takes place in Braga and is organized by three researchers from the University of Minho. Two of them belong to the Systems Engineering and Operational Research Group from the Algoritmi Research Centre and the other to the Statistics, Applied Probability and Operational Research Group from the Centre of Mathematics. The event received more than 50 submissions from 15 countries from Europe, South America and North America. We want to express our gratitude to the invited speaker Panos Pardalos for accepting the invitation and sharing his expertise, helping us to meet the workshop objectives. GOWâ16 would not have been possible without the valuable contribution from the authors and the International ScientiïŹc Committee members. We thank you all. This proceedings book intends to present an overview of the topics that will be addressed in the workshop with the goal of contributing to interesting and fruitful discussions between the authors and participants. After the event, high quality papers can be submitted to a special issue of the Journal of Global Optimization dedicated to the workshop. [...
The K-discretization and K-incident graphs for discretizable Distance Geometry
International audienceThe 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, vâV correspond to the weights duv, for each {u,v}âE. 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