Having problem with Convex.jl

Specific Domains Optimization (Mathematical)
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Hi!
I’m new to Julia. While I was going through the Julia for Data Science course’s ** Numerical Optimization** lecture, got stucked in solve!() unfortunately. Explored in different forums but could not get the solution regarding SCS.Optimizer

After executing:
solve!(problem, SCS.Optimizer)

I’m getting:
MethodError: no method matching solve!(::Problem, ::Type{SCS.Optimizer})

Updated Convex to 0.14.5 but still doesn’t work. Besides, from Convex’s github page, tried the given examples regardins solve!() function, but not luck with non-error output.

I’m from a non-CS background, so you could say me a boomer to programming indeed. Not sure where is the problem, followed the given codes from the course’s .ipynb, still doesn’t work in ma machine.

NB: using
Win10 64bit (Home) OS
Julia 1.7.2 on Jupyter notebook

Capture
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Welcome!

The error message in the screenshot makes it look like you have a very old version of Convex.jl; that references MathProgBase which we stopped using in Convex a few years ago.

How did you update? Try

using Pkg
Pkg.add(PackageSpec(name="Convex", version=v"0.14.5"))

If you get an error, copy the whole message here.

Also, please read this post: Please read: make it easier to help you.

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This often catches people out: when you update your packages, you need to restart Julia for the change to take effect.

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Tnaks a lot for the solution. I updated the Convex.jl likewise but restarting the REPL was the thing that didn’t do. And ya, the attached linked was helpful and will follow afterwards. Thanks for the support!

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Ya! It’s working now! Thanks a lot for helping out. Form now, it is a to go task for me, if something goes wrong, restart the REPL or notebook! Thank you for your support.

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