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## Current Code Features
I see five cross-cutting ways our math and autodiff code can be categorized:
#### Type
- primitive
- reverse mode autodiff
- forward mode autodiff
- mixed mode autodiff
#### …
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To what extent is it possible for automatic differentiation to tell us when the partial derivative of a function with respect to one of its inputs is everywhere zero?
The general approach works by ev…
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I left a comment on Gelman's blog about this but I realized this is the best place to keep track of feature requests. Here's a copy of that request:
Speaking of mixing, Geyer’s parallel tempering see…
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I re-generated the C++ code for a simple model (attached) and reduced it to a minimum test case (also attached). I still get compiler errors expanding the templates.
See https://groups.google.com/for…
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- [x] Eigen.hpp
- [x] EigenDenseBaseAddons.h
- [x] LDLT_factor.hpp
- [x] accumulator.hpp
- [x] add.hpp
- [x] append_col.hpp
- [x] append_row.hpp
- [x] array_builder.hpp
- [x] assign.hpp
- [x] block.hp…
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@scidom, @mlubin, @kmsquire, @powerdistribution : Here I go, for a (probably biased) proposal of a common interface for the forward/reverse mode symbolic derivation function :
- should take an Expre…
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The dual numbers logic at https://github.com/JuliaOpt/Optim.jl/blob/master/src/autodiff.jl and https://github.com/EconForge/NLsolve.jl/blob/master/src/autodiff.jl should be moved here. What sort of in…
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In Optim, we've implemented forward-mode automatic differentiation for computing exact numerical gradients. It seems like this would be helpful for computing Jacobians here. If you're interested in in…
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This is a request for discussion concerning design choices for supporting the `WCONHIST` keyword (history matched producers), and in particular the `RESV` control mode. A little bit of background on …
bska updated
10 years ago
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I opened an issue so that we conclude the ongoing discussion on possible partial rewriting of the forward AD code.
1. Tom, I understand your last message re the gradient. My problem with the output is…