A remark very interesting where transport equations is linked to heat equations. ]]>

Naive question: this matrix M still has to be stored somewhere, so there is still a quadratic memory cost, right? And a super-quadratic computational cost? ]]>

I have a 3d Image volume(MRI) with a label image(1’s and 0’s). I need to calculte the gradient in and outside the object using Neumann boundary conditions. Would your approach work if use the voxels which are the most outer of my label? And to what value would i have to set them then? I am experiencing a lot of difficulties with this part of my implementation of a registration algorithm. If you could help me out a bit that would make my world a whole lot easier!

thanks in advance!!

Rob

]]>In this stack exchange answer [1], I give a somewhat simpler way to understand how the SVM optimization is derived. However, there is another way of deriving SVM. This comes from the ideas developed over a decade or two on Empirical Risk Minimization […

]]>This is a great way to remember DRS. However, there is a technical detail missing: The matrix $M$ is not positive definite. Indeed, for all $x \in X$, we have . Thus, convergence of DRS cannot be deduced from the paper by Rockafellar.

If and , then the metric

can be used in place of $M$. This results in the primal-dual algorithm of Chambolle and Pock (http://www.optimization-online.org/DB_FILE/2010/06/2646.pdf), and weak convergence follows by the results of Rockafellar. Thus, DRS is really a limiting case of a class of primal-dual algorithms. I believe the first paper to notice this connection is by He and Yuan: http://www.math.hkbu.edu.hk/~xmyuan/Paper/HeYuan-SIIMS-2nd.pdf.

Since the paper by He and Yuan, several other metrics have been developed. A few are summarized by Pesquet and Repetti in http://arxiv.org/pdf/1406.6404.pdf, and by myself in http://arxiv.org/pdf/1408.4419v1.pdf

Damek

]]>https://es.wikipedia.org/wiki/Regla_de_Villalobos

I could be in English. I could use a virtual translator, but are not very accurate.

Greetings.

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