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Empirical likelihood and uniform convergence rates for dyadic kernel density estimation
This paper studies the asymptotic properties of and improved inference methods for kernel density estimation (KDE) for dyadic data. We first establish novel uniform convergence rates for dyadic KDE
Self-adaptive inertial extragradient algorithms for solving variational inequality problems
In this paper, we study the strong convergence of two Mann-type inertial extragradient algorithms, which are devised with a new step size, for solving a variational inequality problem with a monotone
Self adaptive inertial extragradient algorithms for solving bilevel pseudomonotone variational inequality problems
TLDR
Two inertial extragradient algorithms for solving a bilevel pseudomonotone variational inequality problem in real Hilbert spaces are introduced and strong convergence theorems of the suggested algorithms are obtained under suitable conditions.
An accelerated hybrid projection method with a self‐adaptive step‐size sequence for solving split common fixed point problems
This paper attempts to solve the split common fixed point problem for demicontractive mappings. We give an accelerated hybrid projection algorithm that combines the hybrid projection method and the
Two modified inertial projection algorithms for bilevel pseudomonotone variational inequalities with applications to optimal control problems
TLDR
Two new algorithms for solving bilevel pseudomonotone variational inequality problems in real Hilbert spaces are investigated and the strong convergence of the proposed algorithms under some suitable conditions imposed on parameters is established.
Strong Convergence of Modified Inertial Mann Algorithms for Nonexpansive Mappings
TLDR
The algorithms and results presented can generalize and extend corresponding results previously known in the literature and be extended to solve fixed point problems in the framework of infinite dimensional Hilbert spaces.
ACCELERATED PROJECTION-BASED FORWARD-BACKWARD SPLITTING ALGORITHMS FOR MONOTONE INCLUSION PROBLEMS
TLDR
Two projection-based algorithms to solve a monotone inclusion problem in infinite dimensional Hilbert spaces are proposed and theorems of strong convergence are obtained under the certain conditions.
Self adaptive viscosity-Type inertial extragradient Algorithms for solving variational inequalities with Applications
In this paper, we introduce two new inertial extragradient algorithms with non-monotonic stepsizes for solving monotone and Lipschitz continuous variational inequality problems in real Hilbert
A General Inertial Viscosity Type Method for Nonexpansive Mappings and Its Applications in Signal Processing
TLDR
The numerical results show that the proposed viscosity algorithms are superior to some related algorithms and the applications to the signal processing are considered.
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