The link you referenced is part of mpmath, which is a dependency of SymPy.

SymPy submodules should generally be symbolic in nature. Purely
numeric applications are out of scope. See
https://github.com/sympy/sympy/wiki/GSoC-2019-Ideas#non-ideas. Unless
you can convince us that your idea requires a symbolic system.

I would suggest looking at the scipy project if you are interested in
numeric optimization. They are participating in GSoC through NumFOCUS.
https://github.com/scipy/scipy/wiki/GSoC-2019-project-ideas

Aaron Meurer

On Tue, Feb 26, 2019 at 11:57 PM Yograj Singh Mandloi <[email protected]> wrote:
>
> Hello Everyone,
>
> The current optimization in sympy is limited to root finding ref. I would 
> like to work on adding more features to the optimization library. I am 
> thinking of implementing various direct-indirect techniques for linear, 
> non-linear dynamic optimization, for example, finding the optimal trajectory 
> where the decision variables are functions of time and states of a dynamic 
> system. Problems can be specified with dynamic or static constraints over 
> parameters and the state variables while selecting the desired optimization 
> techniques (like collocation, shooting or proposed in trajectory optimization 
> research) as input. What do mentors think about this idea?  any suggestion 
> would be appreciated.
>
> I previously implemented some optimization algorithms like genetic algorithm, 
> gradient descent etc from scratch in python. As an aerospace engineering 
> background, I have good of understanding the dynamics optimization.
>
> Thanks
> Regards
> Yograj
>
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