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Q: Least Squares with Total-Variation Regularization - How to Set the Lambda ($\lambda $) Parameter?
I am trying to use total-variation minimization for an image reconstruction problem. Essentially, I am trying to penalize different in the intensity of the two pixels in the reconstructed image. For this, I minimize $|Ax-b|+ \lambda |F(X)|$, where $F(x)= (x_i - x_i+1)^2$ is a quadratic function t...
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E. M. Rains and N. J. A. Sloane, On Cayley's Enumeration of Alkanes (or 4-Valent Trees), Journal of Integer Sequences, Vol. 2 (1999) is only available in HTML, which makes it harder to read than a nice LaTeXified PDF but easy to quote the section which is confusing me: Fix k, and let Th,n be th...
In mathematics, the total variation identifies several slightly different concepts, related to the (local or global) structure of the codomain of a function or a measure. For a real-valued continuous function f, defined on an interval [a, b] ⊂ ℝ, its total variation on the interval of definition is a measure of the one-dimensional arclength of the curve with parametric equation x ↦ f(x), for x ∈ [a, b].
== Historical note ==
The concept of total variation for functions of one real variable was first introduced by Camille Jordan in the paper (Jordan 1881). He used the new concept in order to prove...
In probability theory, the total variation distance is a distance measure for probability distributions. It is an example of a statistical distance metric, and is sometimes just called "the" statistical distance.
== Definition ==
The total variation distance between two probability measures P and Q on a sigma-algebra
F
{\displaystyle {\mathcal {F}}}
of subsets of the sample space
Ω
{\displaystyle \Omega }
is defined via
δ
(
P
...
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