P. M. Pardalos – författare

The satisfiability (SAT) problem is central in mathematical logic, computing theory, and many industrial applications. There has been a strong relationship between the theory, the algorithms, and the applications of the SAT problem. This book aims to bring together work by the best theorists, algorithmists, and practitioners working on the SAT problem and on industrial applications, as well as to enhance the interaction between the three research groups. The book features the application of theoretical/algorithmic results to practical problems and presents practical problems for theoretical/algorithmic study.Major topics covered in the book include practical and industrial SAT problems and benchmarks, significant case studies and applications of the SAT problem and SAT algorithms, new algorithms and improved techniques for satisfiability testing, specific data structures and implementation details of the SAT algorithms, and the theoretical study of the SAT problem and SAT algorithms. It features: a comprehensive review of SAT research work over the past 25 years; the most recent research results; and a spectrum of algorithmic issues and applications.

In the quest to understand and model healthy or sick human body functions, researchers and medical doctors are utilizing more and more quantitative tools and techniques. This trend is advancing the development of the new field of biomedical computing, which is an interface between signal processing, pattern recognition, optimization, nonlinear dynamics, computer science and biology, chemistry and medicine. This volume contains a collection of refereed papers. Included are contributions in genomics, global optimization in biomedicine, computational neuroscience, FMRI, brain dynamics, epileptic seizure prediction and cancer diagnostics.

This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.