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### Description
This is a reposting of the triqs issue: TRIQS/triqs#917 showing that the memory usage building the DLR basis is linear in the inverse temperature $\beta$, preventing the use of DLR …
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In the iterative_methods_sparsity section on orthogonal polynomials, I removed the following code, which we can add back in at some point - hopefully compartmentalized.
```julia
using ApproxFun
…
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Hi,
I tried to train your GCN network using the Chebychev polynomials. However, on my network and features (~10.000 nodes, ~90.000 edges, 24 feature dimensions), my graphics card seems to quickly r…
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... to be able to construct laplacian eigenfunctions.
CC @wcwitt
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Related to #9, here is an outline for a proposal for an interface based around a single function (I call it `qtn` below for "quantized tensor network" but open to suggestions) which at a high level ac…
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for n > 5, even with very low degree (d = 3), calculating the approximate Fekete points and evaluating the Chebyshev polynomials on them and performing the QR takes too much memory. [this is the curre…
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I see that the line to return such a matrix is commented out below. Why?
https://github.com/JuliaApproximation/ApproxFunBase.jl/blob/25ee448cbde61ef8e01f5c9d5af63ac3364a751c/src/Operators/Operator.…
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I see that chaospy does many things around quadrature and orthogonal polynomials. Perhaps you can use what's been implemented in https://github.com/nschloe/quadpy and https://github.com/nschloe/orthop…
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* [ ] sin
* [ ] cos
* [ ] tan
* [ ] asin
* [ ] acos
* [ ] atan
* [ ] atan2
* [ ] sinh
* [ ] cosh
* [ ] tanh
* [ ] asinh
* [ ] acosh
* [ ] atanh
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### 🚀 The feature, motivation and pitch
There are some PyTorch operators that are private (i.e. prefaced with an underscore) that show up in the dispatcher. These operators are problematic, as 1. t…