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I am using a function which uses af.broadcast inside it.
When I use the program in windows PC, I do not see any slowdown of the processing using CPU. However, when I use my linux PC which has both …
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There is `SymEngine.jl`,
`SymPy` and `ParameterizedFunctions` (which relies on `SymEngine` but defines a convenient interface for an ode problem and it's jacobian)
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For many differential equations problems, I need to autodifferentiate a function `f(t,u)`, in both `t` and `u`. But every step of the iteration, I need both derivatives. So what I have been doing is t…
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I was having a first go at fixing a lot of the issues surrounding (coming up with an improved for) *DifferentiableFunctions, and I realized that maybe we want to remove the `autodiff` keyword from `Op…
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There is a `jacobian!` for `f!(y,x)`, but is there no `derivative!` for `f(y,t)` where `t` is a scalar?
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Hello, my name is Antonio and I am a Brazilian electrical engineer currently pursuing my master degree. I am interested in applying to Google Summer of Code 2017 with the proposal of implementing opti…
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In the forthcoming parameter estimation notebook, the idea is to give in the md text the menu of options for each module used to build the optimization problem. For LeastSquaresOptim, this is simple w…
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I'm still a newcomer to autodifferentiation world, but I'm interested in trying it out on some matrix and tensor decomposition optimization problems. As shown in the code below, this involves either r…
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I think we could totally compose tensor operations by using a TH preprocessor like in [category-syntax](https://github.com/gelisam/category-syntax), and avoid the complexity manipulating stacks manual…
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I'm glad the signatures were loosened -- what do you think about limiting to `{T LLA('a', 'a', 'a')
LLA(lat=a°, lon=a°, alt=a)
julia> ENU('a', 'a', 'a')
3-element Geodesy.ENU{Char}:
'a'
'a'
…