Deterministic Global Optimization

Yaroslav D. Sergeyev is the President of the International Society of Global Optimization and Distinguished Professor at the University of Calabria, Italy. He is also a Member of the University International Council and Professor at Lobachevsky Nizhniy Novgorod State University, Russia.  His research interests include numerical analysis, global optimization, infinity computing and calculus, philosophy of computations, set theory, number theory, fractals, parallel computing, and interval analysis.  Dmitri Kvasov is an Assistant Professor at DIMES, University of Calabria, Rende,Italy and Senior Programmer at the Institute of Information Technology, Mathematics and Mechanics at Lobachevsky State University of Nizhny Novgorod, Russia. His research interest includes numerical analysis, continuous global optimization, and infinity computing.

“The topic of the book and the class of algorithms described in the volume are of an indisputable interest … . the book under review gives an important contribution to the existing optimization literature. It will be interesting to a broad scientific community working with application and development of global optimization algorithms. Engineers, researchers, and students will find this volume extremely useful and inspiring.” (Timoleon Kipouros, Journal of Global Optimization, Vol. 80, 2021)“The book is well-organized, self-contained, and technically sound. I think it will be highly requested not only by researchers in the field of global (and local) optimization but also by engineers, graduate and postgraduate students, and practitioners.” (Antanas Zilinskas, Optimization Letters, Vol. 15, 2021)“The authors of this brief book consider global optimization problems, in which the Lipschitz continuity condition is satisfied. … This book will be for sure interesting for the researchers and PhD students working in the area of global optimization.” (Marcin Anholcer, zbMATH 1371.90112, 2017)

• 1. Lipschitz global optimization.- 2. One-dimensional algorithms and their accleration.- 3. Diagonal approach and efficient paritioning strategies.- 4. Global optimization algorithms based on the non-redundant partitions.- References.