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 consist of an incomplete …

The Molecular Distance Geometry Problem (MDGP) consists in finding an embedding in R3
of a nonnegatively weighted simple undirected graph with the property that the Euclidean …

The first systematic overviews on global optimization appeared in 1975–1978 thanks to two
fundamental volumes titled Towards Global Optimization (Dixon & Szegö, 1975, 1978). At …

Data Mining in Agriculture represents a comprehensive effort to provide graduate students
and researchers with an analytical text on data mining techniques applied to agriculture and …

We discuss a discretization-based solution approach for a classic problem in global
optimization, namely the distance geometry problem (DGP). We focus our attention on a …

What do clocks, wireless devices, atoms, and submarines have in common? They move: the
clocks move time forward (so to speak), wireless devices usually move on a plane (like an …

Abstract The Distance Geometry Problem in three dimensions consists in finding an
embedding in R^ 3 of a given nonnegatively weighted simple undirected graph such that …

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 …

Given a weighted, undirected simple graph G=(V, E, d)(where d: E → R _+), the distance
geometry problem (DGP) is to determine an embedding x: V → R^ K such that ∀ {i, j\} ∈ E\; …

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 …