The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Wataru Takahashi - One of the best experts on this subject based on the ideXlab platform.
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weak and strong convergence theorems for new demimetric mappings and the split common fixed point problem in Banach Spaces
Numerical Functional Analysis and Optimization, 2018Co-Authors: Wataru Takahashi, Wataru TakahashiAbstract:AbstractIn this paper, we consider the split common fixed point problem for new demimetric mappings in Banach Spaces. Using the idea of Mann’s iteration, we prove a weak convergence theorem for finding a solution of the split common fixed point problem in Banach Spaces. Furthermore, using the idea of Halpern’s iteration, we obtain a strong convergence theorem for finding a solution of the problem in Banach Spaces. Using these results, we obtain well-known and new weak and strong convergence theorems in Hilbert Spaces and Banach Spaces.
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strong convergence theorems by hybrid methods for the split common null point problem in Banach Spaces
Fixed Point Theory and Applications, 2015Co-Authors: Wataru TakahashiAbstract:In this paper, we consider the split common null point problem in Banach Spaces. Then using the hybrid method and the shrinking projection method in mathematical programming, we prove strong convergence theorems for finding a solution of the split common null point problem in Banach Spaces.
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fixed point theorems for a class of nonlinear mappings related to maximal monotone operators in Banach Spaces
Archiv der Mathematik, 2008Co-Authors: Fumiaki Kohsaka, Wataru TakahashiAbstract:In this paper, the class of nonspreading mappings in Banach Spaces is introduced. This class contains the recently introduced class of firmly nonexpansive type mappings in Banach Spaces and the class of firmly nonexpansive mappings in Hilbert Spaces. Among other things, we obtain a fixed point theorem for a single nonspreading mapping in Banach Spaces. Using this result, we also obtain a common fixed point theorem for a commutative family of nonspreading mappings in Banach Spaces.
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existence and approximation of fixed points of firmly nonexpansive type mappings in Banach Spaces
Siam Journal on Optimization, 2008Co-Authors: Fumiaki Kohsaka, Wataru TakahashiAbstract:A class of nonlinear operators in Banach Spaces is proposed. We call each operator in this class a firmly nonexpansive-type mapping. This class contains the classes of firmly nonexpansive mappings in Hilbert Spaces and resolvents of maximal monotone operators in Banach Spaces. We study the existence and approximation of fixed points of firmly nonexpansive-type mappings in Banach Spaces.
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weak and strong convergence theorems for relatively nonexpansive mappings in Banach Spaces
Fixed Point Theory and Applications, 2004Co-Authors: Shinya Matsushita, Wataru TakahashiAbstract:We first introduce an iterative sequence for finding fixed points of relatively nonexpansive mappings in Banach Spaces, and then prove weak and strong convergence theorems by using the notion of generalized projection. We apply these results to the convex feasibility problem and a proximal-type algorithm for monotone operators in Banach Spaces.
Jun Zhang - One of the best experts on this subject based on the ideXlab platform.
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vector valued reproducing kernel Banach Spaces with applications to multi task learning
Journal of Complexity, 2013Co-Authors: Haizhang Zhang, Jun ZhangAbstract:Motivated by multi-task machine learning with Banach Spaces, we propose the notion of vector-valued reproducing kernel Banach Spaces (RKBSs). Basic properties of the Spaces and the associated reproducing kernels are investigated. We also present feature map constructions and several concrete examples of vector-valued RKBSs. The theory is then applied to multi-task machine learning. Especially, the representer theorem and characterization equations for the minimizer of regularized learning schemes in vector-valued RKBSs are established.
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Reproducing kernel Banach Spaces for machine learning
2009 International Joint Conference on Neural Networks, 2009Co-Authors: Haizhang Zhang, Yuesheng Xu, Jun ZhangAbstract:Reproducing kernel Hilbert space (RKHS) methods have become powerful tools in machine learning. However, their kernels, which measure similarity of inputs, are required to be symmetric, constraining certain applications in practice. Furthermore, the celebrated representer theorem only applies to regularizers induced by the norm of an RKHS. To remove these limitations, we introduce the notion of reproducing kernel Banach Spaces (RKBS) for pairs of reflexive Banach Spaces of functions by making use of semi-inner-products and the duality mapping. As applications, we develop the framework of RKBS standard learning schemes including minimal norm interpolation, regularization network, and support vector machines. In particular, existence, uniqueness and representer theorems are established.
Trygve Helgaker - One of the best experts on this subject based on the ideXlab platform.
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generalized kohn sham iteration on Banach Spaces
Journal of Chemical Physics, 2018Co-Authors: Andre Laestadius, Markus Penz, Erik I Tellgren, Michael Ruggenthaler, Simen Kvaal, Trygve HelgakerAbstract:A detailed account of the Kohn–Sham (KS) algorithm from quantum chemistry, formulated rigorously in the very general setting of convex analysis on Banach Spaces, is given here. Starting from a Levy–Lieb-type functional, its convex and lower semi-continuous extension is regularized to obtain differentiability. This extra layer allows us to rigorously introduce, in contrast to the common unregularized approach, a well-defined KS iteration scheme. Convergence in a weak sense is then proven. This generalized formulation is applicable to a wide range of different density-functional theories and possibly even to models outside of quantum mechanics.A detailed account of the Kohn–Sham (KS) algorithm from quantum chemistry, formulated rigorously in the very general setting of convex analysis on Banach Spaces, is given here. Starting from a Levy–Lieb-type functional, its convex and lower semi-continuous extension is regularized to obtain differentiability. This extra layer allows us to rigorously introduce, in contrast to the common unregularized approach, a well-defined KS iteration scheme. Convergence in a weak sense is then proven. This generalized formulation is applicable to a wide range of different density-functional theories and possibly even to models outside of quantum mechanics.
Shinya Matsushita - One of the best experts on this subject based on the ideXlab platform.
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weak and strong convergence theorems for relatively nonexpansive mappings in Banach Spaces
Fixed Point Theory and Applications, 2004Co-Authors: Shinya Matsushita, Wataru TakahashiAbstract:We first introduce an iterative sequence for finding fixed points of relatively nonexpansive mappings in Banach Spaces, and then prove weak and strong convergence theorems by using the notion of generalized projection. We apply these results to the convex feasibility problem and a proximal-type algorithm for monotone operators in Banach Spaces.
Erdal Karapinar - One of the best experts on this subject based on the ideXlab platform.
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fixed point theorems in cone Banach Spaces
Fixed Point Theory and Applications, 2009Co-Authors: Erdal KarapinarAbstract:In this manuscript, a class of self-mappings on cone Banach Spaces which have at least one fixed point is considered. More precisely, for a closed and convex subset of a cone Banach space with the norm , if there exist , , and satisfies the conditions and for all , then has at least one Fixed point.