Results 1 to 10 of about 26,892 (218)
The American Mathematical Monthly, 2022 The recursively-constructed family of Mandelbrot matrices $M_n$ for $n=1$, $2$, $\ldots$ have nonnegative entries (indeed just $0$ and $1$, so each $M_n$ can be called a binary matrix) and have eigenvalues whose negatives $-λ= c$ give periodic orbits under the Mandelbrot iteration, namely $z_k = z_{k-1}^2+c$ with $z_0=0$, and are thus contained in the ...
Neil J. Calkin +4 more
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Linearizable Eigenvector Nonlinearities
SIAM Journal on Matrix Analysis and Applications, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rob Claes +3 more
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Eigenvectors and Reconstruction [PDF]
The Electronic Journal of Combinatorics, 2007 In this paper, we study the simple eigenvectors of two hypomorphic matrices using linear algebra. We also give new proofs of results of Godsil and McKay.
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Convergence of Eigenvector Continuation [PDF]
Physical Review Letters, 2021 Eigenvector continuation is a computational method that finds the extremal eigenvalues and eigenvectors of a Hamiltonian matrix with one or more control parameters. It does this by projection onto a subspace of eigenvectors corresponding to selected training values of the control parameters.
Avik Sarkar, Dean Lee
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On the eigenvectors of p-Laplacian [PDF]
Machine Learning, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dijun Luo +3 more
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Eigenvectors of Tensors—A Primer [PDF]
Acta Applicandae Mathematicae, 2018 We give an introduction to the theory and to some applications of eigenvectors of tensors (in other words, invariant one-dimensional subspaces of homogeneous polynomial maps), including a review of some concepts that are useful for their discussion. The intent is to give practitioners an overview of fundamental notions, results and techniques.
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The E-eigenvectors of tensors [PDF]
Linear and Multilinear Algebra, 2013 17 ...
Hu, Shenglong, Qi, Liqun
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Journal of Statistical Mechanics: Theory and ExperimentAbstract Among the performance-enhancing procedures for Hopfield-type networks that implement associative memory, Hebbian unlearning (HU) (or dreaming) strikes for its simplicity and lucid biological interpretation. However, it does not easily lend to a clear analytical understanding.
Benedetti, Marco +3 more
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Linearizability of eigenvector nonlinearities
CoRR, 2021 We present a method to linearize, without approximation, a specific class of eigenvalue problems with eigenvector nonlinearities (NEPv), where the nonlinearities are expressed by scalar functions that are defined by a quotient of linear functions of the eigenvector.
Claes, Rob +3 more
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Galerkin eigenvector approximations [PDF]
Mathematics of Computation, 2000 How close are Galerkin eigenvectors to the best approximation available out of the trial subspace? Under a variety of conditions the Galerkin method gives an approximate eigenvector that approaches asymptotically the projection of the exact eigenvector onto the trial subspace—and this occurs more rapidly than the underlying rate of
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