In this post, I will try to compare and contrast Julia, R, and Python via a simple maximum likelihood optimization problem which is motivated by a problem from the credit risk domain and is discussed in more detail in this post.
TL;DR
For such a simple optimization problem, R, Julia, and Python/SciPy will all do a competent job, so there is no clear winner. However as noted by Julia discourse member ChrisRackauckas
if you wish to solve only a single optimization problem and that problem takes <10 seconds, then Julia’s long initial compilation is something you want to avoid. For long enough problems, or for solving multiple optimization problems, the compilation time is not noticeable.
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