Results 1 to 10 of about 153 (127)
Quasi-Contraction Maps in Subordinate Semimetric Spaces
Throughout this study, we discuss the subordinate Pompeiu–Hausdorff metric (SPHM) in subordinate semimetric spaces. Moreover, we present a well-behaved quasi-contraction (WBQC) to solve quasi-contraction (QC) problems in subordinate semimetric spaces ...
Areej Alharbi +2 more
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On generalizations of some fixed point theorems in semimetric spaces with triangle functions
In the present study, we prove generalizations of Banach, Kannan, Chatterjea, Ćirić-Reich-Rus fixed point theorems, as well as of the fixed point theorem for mapping contracting perimeters of triangles.
Evgeniy Petrov +2 more
doaj +5 more sources
Two metrics on rooted unordered trees with labels [PDF]
Background The early development of a zygote can be mathematically described by a developmental tree. To compare developmental trees of different species, we need to define distances on trees.
Yue Wang
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We establish three types of nonlinear fixed point theorems in regular semimetric spaces. First, we generalize Miculescu and Mihail’s result, thereby unifying the Matkowski fixed point theorem and the Istrăţescu fixed point theorem concerning convex contractions within the semimetric framework.
Shu-Min Lu, Fei He
exaly +3 more sources
Hutchinson’s theorem in semimetric spaces
AbstractOne of the important consequences of the Banach fixed point theorem is Hutchinson’s theorem which states the existence and uniqueness of fractals in complete metric spaces. The aim of this paper is to extend this theorem for semimetric spaces using the results of Bessenyei and Páles published in 2017.
Zsolt Pales, Pales Zsolt
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Subordinate Semimetric Spaces and Fixed Point Theorems
We introduce the concept of subordinate semimetric space. Such notion includes the concept of RS-space introduced by Roldán and Shahzad; therefore the concepts of Branciari’s generalized metric space and Jleli and Samet’s generalized metric space are ...
José Villa-Morales
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Contingency Space: A Semimetric Space for Classification Evaluation
In Machine Learning, a supervised model's performance is measured using the evaluation metrics. In this study, we first present our motivation by revisiting the major limitations of these metrics, namely one-dimensionality, lack of context, lack of intuitiveness, uncomparability, binary restriction, and uncustomizability of metrics.
Azim Ahmadzadeh +2 more
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Weak Similarities of Finite Ultrametric and Semimetric Spaces
14 pages, 2 figures.
Evgeniy Petrov, Petrov Evgeniy
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Uniqueness of best proximity pairs and rigidity of semimetric spaces
32 pages, 10 ...
Oleksiy Dovgoshey +2 more
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Weak similarities of metric and semimetric spaces
23 pages, 1 ...
Oleksiy Dovgoshey +2 more
exaly +4 more sources

