Fast Derivatives in Julia with ForwardDiff
ForwardDiff is a small tool that lets Julia programs get derivatives quickly and with little fuss.
It can compute slopes, gradients and more for code, even when the code uses unusual number types or complex math.
Many people see it as fast as low-level languages, but it stay easy to use.
It will recompile your code so the math runs smooth and can handle higher-order problems other tools struggle with.
In tests with larger problems ForwardDiff sometimes beats similar tools written for Python, so you might notice jobs finish faster.
Researchers and engineers use it inside modeling tools like JuMP for optimization, in astronomy, stats, and simulations.
Over forty projects on GitHub already rely on it, showing it helped many teams solve real problems.
Try it if you want quick derivatives in Julia, make models run faster, and explore new ideas in science and engineering.
It may change how you build math programs, and you could see speed gains right away.
Read article comprehensive review in Paperium.net:
Forward-Mode Automatic Differentiation in Julia
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