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stevensoojin.kim | ||
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lucatrevisan.wordpress.com
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| | | | | Welcome to phase two of in theory, in which we again talk about math. I spent last Fall teaching two courses and getting settled, I mostly traveled in January and February, and I have spent the last two months on my sofa catching up on TV series. Hence I will reach back to last Spring,... | |
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djalil.chafai.net
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| | | | | This post is mainly devoted to a probabilistic proof of a famous theorem due to Schoenberg on radial positive definite functions. Let us begin with a general notion: we say that \( {K:\mathbb{R}^d\times\mathbb{R}^d\rightarrow\mathbb{R}} \) is a positive definite kernel when \[ \forall n\geq1, \forall x_1,\ldots,x_n\in\mathbb{R}^d, \forall c\in\mathbb{C}^n, \quad\sum_{i=1}^n\sum_{j=1}^nc_iK(x_i,x_j)\bar{c}_j\geq0. \] When \( {K} \) is symmetric, i.e. \( {K(x,y)=K(y,x)} \) for... | |
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fabricebaudoin.blog
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| | | | | In this lecture, we studySobolev inequalities on Dirichlet spaces. The approach we develop is related to Hardy-Littlewood-Sobolev theory The link between the Hardy-Littlewood-Sobolev theory and heat kernel upper bounds is due to Varopoulos, but the proof I give below I learnt it from my colleague RodrigoBañuelos. It bypasses the Marcinkiewicz interpolation theorem,that was originally used... | |
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peterbloem.nl
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| | | [AI summary] The pseudo-inverse is a powerful tool for solving matrix equations, especially when the inverse does not exist. It provides exact solutions when they exist and least squares solutions otherwise. If multiple solutions exist, it selects the one with the smallest norm. The pseudo-inverse can be computed using the singular value decomposition (SVD), which is numerically stable and handles cases where the matrix does not have full column rank. The SVD approach involves computing the SVD of the matrix, inverting the non-zero singular values, and then reconstructing the pseudo-inverse using the modified SVD components. This method is preferred due to its stability and ability to handle noisy data effectively. | ||
