Optim.jl

> It's not JUST google and apple, I'm sure they have enough cash and expertise and will to create enough momentum to attract more domain experts in other areas. Maybe, but I'm doubtful. Scientific domain experts flock to languages like Python, Julia, Matlab, R, etc. because they're interactive and allow them to quickly iterate on ideas, query data, produce plots, etc. Swift is not much of an interactive language and is not built around that kind of repl driven experience. > In particular, do you have another example aside from DifferentialEquations.jl ? Sure, here's a smattering of high quality packages made by and for research scientists: https://github.com/JuliaApproximation/ApproxFun.jl https://github.com/BioJulia https://github.com/JuliaDiffEq/ModelingToolkit.jl https://github.com/crstnbr/MonteCarlo.jl https://gith

Just reading what everyone does may work. Here's a tiny package: https://github.com/ChrisRackauckas/EllipsisNotation.jl I think that `/src/Name.jl` must have the main module, and `/test/runtests,jl` tests. And the package manager cares about `Project.toml`. But beyond this there are no real rules enforced, although there really seems to be one way to do things. Here's a much bigger project, organised the same way. `include(file.jl)` literally copies in the text, and it's somewhat conventional to collect all imports & exports in the main file: https://github.com/JuliaNLSolvers/Optim.jl/blob/master/src/O... Still no sub-modules. No files included in mysterious locations. Methods being defined for functions from elsewhere are all qualified, like `Base.show(io::IO, t::OptimizationState) = ...`

This is very similar to dual numbers [1] which I find even easier to reason about. There is an implementation of dual numbers in Julia [2] that is quite fun to play around with. The Optim.jl package [3] uses this to get better derivatives than finite differencing. [1] http://en.wikipedia.org/wiki/Dual_number [2] https://github.com/JuliaDiff/DualNumbers.jl [3] https://github.com/JuliaOpt/Optim.jl

For anyone interested in using Julia for numerical optimization/mathematical programming, see http://juliaopt.org/. We have pure-Julia implementations of standard unconstrained methods [1] as well as a domain-specific modeling language [2] for (integer) linear programming with links to open-source and commercial solvers. Julia's performance and advanced language features such as metaprogramming really make it a great language for optimization [3]. [1] https://github.com/JuliaOpt/Optim.jl [2] https://github.com/JuliaOpt/JuMP.jl [3] http://www.mit.edu/~mlubin/juliacomputing.pdf