Demian Battaglia Boeken

Focusing on the optimization of random Constraint Satisfaction Problems (CSPs), the book explores the challenges posed by numerous competing conditions in various applications, from Sudokus to digital design. It introduces the Survey Propagation algorithm, which effectively tackles difficult instances through probabilistic message exchanges in factor graphs, outperforming traditional methods. The text also examines the typical-case complexity of random K-Satisfiability Problems and discusses innovative uses of Survey Propagation in lossy data compression, transforming complexity into a computational advantage.

This book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience. While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a problem of interest in a wide variety of domains, including econometrics, epidemiology, and climatology, to cite only a few.< The book is designed for the mathematics, physics and computer science communities that carry out research in neuroscience problems. The content is also suitable for the machine learning community because it exemplifies how to approach the same problem from different perspectives.