Lipschitz Functions, 1st ed. 2019 Lecture Notes in Mathematics Series, Vol. 2241

The aim of this book is to present various facets of the theory and applications of Lipschitz functions, starting with classical and culminating with some recent results. Among the included topics we mention: characterizations of Lipschitz functions and relations with other classes of functions, extension results for Lipschitz functions and Lipschitz partitions of unity, Lipschitz free Banach spaces and their applications, compactness properties of Lipschitz operators, Bishop-Phelps type results for Lipschitz functionals, applications to best approximation in metric and in metric linear spaces, Kantorovich-Rubinstein norm and applications to duality in the optimal transport problem, Lipschitz mappings on geodesic spaces.

Stefan Cobzas is Emeritus Professor at the Babes-Bolyai University, Department of Mathematics, Cluj-Napoca, Romania. He graduated from the same university in 1968 and obtained a Ph.D. in 1979. His scientific interests concern mainly applied functional analysis -- optimization and best approximation in Banach spaces. In the last years he worked on some problems in asymmetric functional analysis and published several papers and a book (in the series Frontiers in Mathematics, Birkhauser-Springer, 2013) on this topic.

Radu Miculescu is Professor at Transilvania University of Brasov, Romania. He graduated from Bucharest University, Romania, in 1992 and obtained his Ph.D in 1999 from the same university with a thesis concerning Lipschitz functions. In the last period his scientific interest includes Hutchinson-Barnsley fractals.

Adriana Nicolae is Associate Professor at the Babes-Bolyai University, Department of Mathematics, Cluj-Napoca, Romania. She graduated in 2007 from the same university and focused during her Ph.D. on various aspects in metric fixed point and best approximation theory mainly in the setting of geodesic metric spaces. In the last years she also addressed problems in areas such as geometry and analysis in metric spaces, optimization, or proof mining.