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www.ethanepperly.com | ||
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nhigham.com
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| | | | | For an $latex n\times n$ matrix $latex \notag A = \begin{bmatrix} A_{11} & A_{12} \\ A_{21} & A_{22} \end{bmatrix} \qquad (1) $ with nonsingular $latex (1,1)$ block $LATEX A_{11}$ the Schur complement is $LATEX A_{22} - A_{21}A_{11}^{-1}A_{12}$. It is denoted by $LATEX A/A_{11}$. The block with respect to which the Schur complement is taken need... | |
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fa.bianp.net
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| | | | | There's a fascinating link between minimization of quadratic functions and polynomials. A link that goes deep and allows to phrase optimization problems in the language of polynomials and vice versa. Using this connection, we can tap into centuries of research in the theory of polynomials and shed new light on ... | |
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nickhar.wordpress.com
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| | | | | 1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B... | |
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hadrienj.github.io
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| | | In this post, we will see special kinds of matrix and vectors the diagonal and symmetric matrices, the unit vector and the concept of orthogonality. | ||
