Uniqueness of best simultaneous approximation and strictly interpolating subspaces
If F,G are subsets of a normed linear space E,F bounded, a best simultaneous approximation to F in G is \(y_ 0\in G\) minimizing \(r(y,G)=\sup_{x\in F}\| x-y\|\). The set of best simultaneous approximations to F in G is denoted \(Z_ G(F)\) and called also the ''Chebyshev center'' for F in G, and \(r_ G(F)=\inf_{y\in G}r(y,F)\) is called the ''Chebyshev
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ABSTRACT The contribution deals with algebraic multigrid (AMG) based preconditioning methods for the iterative solution of a coupled linear system of equations arising in numerical simulations of failure of quasi‐brittle materials using generalized continuum approaches.
Nasser Alkmim +4 more
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Interpolating subspaces in approximation theory
Ault, D.A +3 more
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Extensions to Extended Tight‐Binding Methods for Transition‐Metal Containing Systems
We present a new GFN2‐xTB implementation with a geometric direct minimization scheme and a Hubbard‐U correction. We demonstrate that the Hubbard correction improves linearity of the elctronic energy, stabilizes SCF convergence, and enables more accurate spin‐gap predictions in narrow application domains such as specific iron‐containing complexes ...
Siyavash Moradi +3 more
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Common population codes produce extremely nonlinear neural manifolds. [PDF]
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Joint Estimation Method of DOD and DOA of Bistatic Coprime Array MIMO Radar for Coherent Targets Based on Low-Rank Matrix Reconstruction. [PDF]
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Enhancing unsupervised bearing fault diagnosis through structured prediction in latent subspace. [PDF]
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PSO-DT based BagDT: a robust lightweight ensemble framework for efficient feature selection and DDoS attack detection in IoT environment. [PDF]
Shirley JJ, Priya M.
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Unifying machine learning and interpolation theory via interpolating neural networks. [PDF]
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