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＜電子ブック＞
Fixed Point Theory in Probabilistic Metric Spaces / by O. Hadzic, E. Pap
(Mathematics and Its Applications ; 536)

版	1st ed. 2001.
出版者	(Dordrecht : Springer Netherlands : Imprint: Springer)
出版年	2001
本文言語	英語
大きさ	IX, 273 p : online resource
著者標目	*Hadzic, O author Pap, E author SpringerLink (Online service)
件　名	LCSH:Operator theory LCSH:Probabilities LCSH:Functional analysis LCSH:Topology LCSH:Mathematical logic FREE:Operator Theory FREE:Probability Theory FREE:Functional Analysis FREE:Topology FREE:Mathematical Logic and Foundations
一般注記	1 Triangular norms -- 2 Probabilistic metric spaces -- 3 Probabilistic B-contraction principles for single-valued mappings -- 4 Probabilistic B-contraction principles for multi-valued mappings -- 5 Hicks’ contraction principle -- 6 Fixed point theorems in topological vector spaces and applications to random normed spaces Fixed point theory in probabilistic metric spaces can be considered as a part of Probabilistic Analysis, which is a very dynamic area of mathematical research. A primary aim of this monograph is to stimulate interest among scientists and students in this fascinating field. The text is self-contained for a reader with a modest knowledge of the metric fixed point theory. Several themes run through this book. The first is the theory of triangular norms (t-norms), which is closely related to fixed point theory in probabilistic metric spaces. Its recent development has had a strong influence upon the fixed point theory in probabilistic metric spaces. In Chapter 1 some basic properties of t-norms are presented and several special classes of t-norms are investigated. Chapter 2 is an overview of some basic definitions and examples from the theory of probabilistic metric spaces. Chapters 3, 4, and 5 deal with some single-valued and multi-valued probabilistic versions of the Banach contraction principle. In Chapter 6, some basic results in locally convex topological vector spaces are used and applied to fixed point theory in vector spaces. Audience: The book will be of value to graduate students, researchers, and applied mathematicians working in nonlinear analysis and probabilistic metric spaces HTTP:URL=https://doi.org/10.1007/978-94-017-1560-7

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