Nelson Maculan – författare

Most books about global optimization describe the theory of the algorithms, whereas a given implementation’s quality never depends exclusively on the theoretical soundness of the algorithms that are implemented. The literature rarely discusses the tuning of algorithmic parameters, implementation tricks, software architectures, and the embedding of local solvers within global solvers. And yet, there are many good software implementations "out there” from which the entire community could learn something.

The scope of this book is moving a few steps toward the systematization of the path that goes from the invention to the implementation and testing of a global optimization algorithm. Some of the contributors to the book are famous and some are less well-known, but all are experts in the discipline of actually getting global optimization to work. Thus, the papers in this book address the following topics:

- Descriptions of new implementations of general-purpose or problem-specific global optimization algorithms

- New algorithms in global optimization (some with numerical results and a discussion of the implementation)

- Surveys discussing existing software packages.

This volume is a collection of research surveys on the Distance Geometry Problem (DGP) and its applications. It will be divided into three parts: Theory, Methods and Applications. Each part  will contain at least one survey and several research papers.

The first part, Theory, will deal with theoretical aspects of the DGP, including a new class of problems and the study of its complexities as well as the relation between DGP and other related topics, such as: distance matrix theory, Euclidean distance matrix completion problem, multispherical structure of distance matrices, distance geometry and geometric algebra, algebraic distance geometry theory, visualization of K-dimensional structures in the plane, graph rigidity, and theory of discretizable DGP: symmetry and complexity.

The second part, Methods, will discuss mathematical and computational properties of methods developed to the problems considered in the first chapter including continuous methods (based on Gaussian and hyperbolic smoothing, difference of convex functions, semidefinite programming, branch-and-bound), discrete methods (based on branch-and-prune, geometric build-up, graph rigidity), and also heuristics methods (based on simulated annealing, genetic algorithms, tabu search, variable neighborhood search).

Applications will comprise the third part and will consider applications of DGP to NMR structure calculation, rational drug design, molecular dynamics simulations, graph drawing and sensor network localization.

This volume will be the first edited book on distance geometry and applications. The editors are in correspondence with the major contributors to the field of distance geometry, including important research centers in molecular biology such as Institut Pasteur in Paris.