Results 1 to 10 of about 51,655,942 (350)

New Iterative Algorithm for Solving Constrained Convex Minimization Problem and Split Feasibility Problem

open access: yesEuropean Journal of Mathematical Analysis, 2021
The purpose of this paper is to introduce a new iterative algorithm to approximate the fixed points of almost contraction mappings and generalized α-nonexpansive mappings.
Austine Efut Ofem   +2 more
doaj   +1 more source

Fixed Point Theory for Multi-Valued Feng–Liu–Subrahmanyan Contractions

open access: yesAxioms, 2022
In this paper, we consider several problems related to the so-called multi-valued Feng–Liu–Subrahmanyan contractions in complete metric spaces. Existence of the fixed points and of the strict fixed points, as well as data dependence and stability ...
Claudia Luminiţa Mihiţ   +2 more
doaj   +1 more source

On the convergence, stability and data dependence results of the JK iteration process in Banach spaces

open access: yesOpen Mathematics, 2023
This article analyzes the JK iteration process with the class of mappings that are essentially endowed with a condition called condition (E). The convergence of the iteration toward a fixed point of a specific mapping satisfying the condition (E) is ...
Ullah Kifayat   +5 more
doaj   +1 more source

Modelling, characterization of data-dependent and process-dependent errors in DNA data storage [PDF]

open access: yesIEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021
Abstract Motivation Using DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content.
Yixin Wang 0005   +4 more
openaire   +2 more sources

Some New Results on Convergence, Weak w2-Stability and Data Dependence of Two Multivalued Almost Contractive Mappings in Hyperbolic Spaces

open access: yesMathematics, 2022
In this article, we introduce a new mixed-type iterative algorithm for approximation of common fixed points of two multivalued almost contractive mappings and two multivalued mappings satisfying condition (E) in hyperbolic spaces.
Austine Efut Ofem   +4 more
doaj   +1 more source

Characterizing Data Dependence Constraints for Dynamic Reliability Using n-Queens Attack Domains [PDF]

open access: yesLeibniz Transactions on Embedded Systems, 2017
As data centers attempt to cope with the exponential growth of data, new techniques for intelligent, software-defined data centers (SDDC) are being developed to confront the scale and pace of changing resources and requirements.
Rozier, Eric W. D.   +2 more
doaj   +1 more source

Predicting Data-Dependent Jitter [PDF]

open access: yesIEEE Transactions on Circuits and Systems II: Express Briefs, 2004
An analysis for calculating data-dependent jitter (DDJ) in a first-order system is introduced. The predicted DDJ features unique threshold crossing times with self-similar geometry. An approximation for DDJ in second-order systems is described in terms of the damping factor and natural frequency.
James F. Buckwalter   +2 more
openaire   +3 more sources

Existence, uniqueness, Ulam–Hyers–Rassias stability, well-posedness and data dependence property related to a fixed point problem in gamma-complete metric spaces with application to integral equations

open access: yesNonlinear Analysis, 2022
In this paper, we study a fixed point problem for certain rational contractions on γ-complete metric spaces. Uniqueness of the fixed point is obtained under additional conditions. The Ulam–Hyers–Rassias stability of the problem is investigated.
Binayak S. Choudhury   +3 more
doaj   +1 more source

On Some New Multivalued Results in the Metric Spaces of Perov’s Type

open access: yesMathematics, 2020
The purpose of this paper is to present some new fixed point results in the generalized metric spaces of Perov’s sense under a contractive condition of Hardy−Rogers type.
Liliana Guran   +5 more
doaj   +1 more source

Data-Dependent Randomized Smoothing

open access: yesCoRR, 2020
Randomized smoothing is a recent technique that achieves state-of-art performance in training certifiably robust deep neural networks. While the smoothing family of distributions is often connected to the choice of the norm used for certification, the parameters of these distributions are always set as global hyper parameters independent from the input
Motasem Alfarra   +3 more
openaire   +5 more sources

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