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fa.bianp.net | ||
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tcsmath.github.io
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| | | | | Continuous-time mirror descent analysis | |
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statisticaloddsandends.wordpress.com
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| | | | | Let's say we have a convex, differentiable function $latex f: \mathbb{R}^p \rightarrow \mathbb{R}$ and that we want to minimize it. In this previous post, we saw that a gradient descent step at time $latex t$ (from point $latex \beta^t$) could be viewed as the minimizer of a quadratic approximation of $latex f$ at $latex \beta^t$:... | |
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fabricebaudoin.blog
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| | | | | In this section, we consider a diffusion operator $latex L=\sum_{i,j=1}^n \sigma_{ij} (x) \frac{\partial^2}{ \partial x_i \partial x_j} +\sum_{i=1}^n b_i (x)\frac{\partial}{\partial x_i}, $ where $latex b_i$ and $latex \sigma_{ij}$ are continuous functions on $latex \mathbb{R}^n$ and for every $latex x \in \mathbb{R}^n$, the matrix $latex (\sigma_{ij}(x))_{1\le i,j\le n}$ is a symmetric and non negative matrix. Our... | |
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alok.github.io
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| | | Alok Singh's Blog | ||
