I am looking for examples: show me some real computational power in seven or fewer lines of Julia that read clearly. It is ok to be using other packages, though no more than four (and it is ok to use fewer). Each example is intended to make a compelling case for Julia to skilled practitioners of w…

Another neat use of function composition: Minimizing a convex function f(x) over an affine set Ax = b using a projected Newton’s method and autodiff: using LinearAlgebra; using ForwardDiff: gradient, hessian # Imports n, m = 30, 10 …

[image] maxkapur: But is the splatting worth the cost? If performance matters you can use foldl as proposed upthread by @FedericoStra : julia> F(y) = 2y; julia> Fⁿ(x, n) = foldl((y,_) -> F(y), 1:n; init=x); julia> Fⁿ(1, 10) 1024 [image] maxkapur: And is there a way to have it return th…

Here are 6 lines of code (excluding the import and using statements) that define linear, quadratic and cubic Bezier splines using higher order functions. using Plots import Base.+, Base.*, Base./ +(f, g) = x -> f(x) + g(x) *(t::Number, g) = x -> t * g(x) interpolate(a, b) = t -> (1.0-t)*a + t*b b…

This is a great thread. Might worth contributing to One Liner Hub . It has no Julia code, so it is time to have some.

I propose herein an interesting enhancement of the classic moving (rolling or running) average filter (MA filter). The key thing about the MA filter is that it can be implemented with a simple algorithm that is extremely fast . That algorithm is written below but with improved boundary conditions. …

[image] rafael.guerra: If you know of a reference for this technique, please let me know. All reflection references I found were in a density estimation context. Here you use it as an extrapolation (prediction) method. Visually appealing, but justified? BTW, in x0, the endpoints of x are c…

[image] Bardo: BTW, in x0, the endpoints of x are counted twice. Well spotted! x0 has now been improved in code above. And thanks for the references.

Not seven lines, but I still enjoy it every time I use it: using Symbolics @syms x y z import LinearAlgebra: dot, (×) const ∂ = Symbolics.derivative struct ∇; end ∇(ψ::T) where T = [∂(ψ,x), ∂(ψ,y), ∂(ψ,z)] dot(::Type{∇}, u) = ∂(u[1],x) + ∂(u[2],y) + ∂(u[3],z) (×)(::Type{∇}, u) = [∂(u[3],y) - ∂(u[2]…

Checked the Laplacian: Δ(u) == ∇⋅∇(u) # and got it is true

Seven Lines of Julia (examples sought)

General Usage

goerch November 20, 2021, 12:47am 129

I’d like to mention @aplavin `s comment Y.A.t.Q : Yet Another @threads Question - #13 by aplavin which looks like the start of a new search engine.

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Seven Lines of Julia (examples sought)

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