Antonio Mucherino

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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 set of distances and the output is a set of points in Euclidean space realizing those given distances. We survey the theory of Euclidean distance geometry and its most important(More)
We consider the Discretizable Molecular Distance Geometry Problem (DMDGP), which consists in a subclass of instances of the distance geometry problem related to molecular conformations for which a combinatorial reformulation can be supplied. We investigate the performances of two different algorithms for solving the DMDGP. The first one is the Branch and(More)
Given a simple weighted undirected graph G = (V,E, d) with d : E → R+, the Molecular Distance Geometry Problem (MDGP) consists in finding an embedding x : V → R such that ||xu −xv|| = duv for each {u, v} ∈ E. We show that under a few assumptions usually satisfied in proteins, the MDGP can be formulated as a search in a discrete space. We call this MDGP(More)
NMR experiments are able to provide some of the distances between pairs of hydrogen atoms in molecular conformations. The problem of finding the coordinates of such atoms is known as the molecular distance geometry problem. This problem can be reformulated as a combinatorial optimization problem and efficiently solved by an exact algorithm. To this purpose,(More)
Consistent biclusterings of sets of data are useful for solving feature selection and classification problems. The problem of finding a consistent biclustering can be formulated as a combinatorial optimization problem, and it can be solved by the employment of a recently proposed VNS-based heuristic. In this context, the concept of β-consistent(More)
This survey covers some very recent applications of data mining techniques in the field of agriculture. This is an emerging research field that is experiencing a constant development. In this paper, we first present two applications in this field in details; in particular, we consider the problem of discovering problematic wine fermentations at the early(More)
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(More)
The Discretizable Molecular Distance Geometry Problem (DMDGP) consists in a subclass of distance geometry instances (related to molecules) that can be solved by combinatorial optimization. A modified version of the Branch and Prune (BP) algorithm, previously proposed for solving these instances, is presented, where it is supposed that exact distances are(More)
The molecular distance geometry problem can be formulated as the problem of finding an immersion in R<sup>3</sup> of a given undirected, nonnegatively weighted graph <i>G</i>. In this paper, we discuss a set of graphs <i>G</i> for which the problem may also be formulated as a combinatorial search in discrete space. This is theoretically interesting as an(More)