# Optimization and arbitrary precision

**URL:** <https://discourse.julialang.org/t/optimization-and-arbitrary-precision/21801>\
**Category:** Optimization (Mathematical)\
**Tags:** question\
**Created:** [March 12, 2019, 11:21pm UTC](https://discourse.julialang.org/t/optimization-and-arbitrary-precision/21801 "2019-03-12T23:21:12Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![Andre\_Mello](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/andre_mello/32/7333_2.png) [@Andre\_Mello](https://discourse.julialang.org/u/Andre_Mello)\
**Post date:** [March 13, 2019, 12:52am UTC](https://discourse.julialang.org/t/optimization-and-arbitrary-precision/21801/3 "2019-03-13T00:52:40Z")

</div>

Thanks for the heads up @rdeits!

However, I’ll be mostly working with Single Shooting/Multi Shooting (and equivalent) methods for estimating parameters from Ordinary Differential Equations extracted from chemical kinetics models. Something along the lines of my [previous question](https://discourse.julialang.org/t/lsqfit-jl-error-on-ode-data-fitting/21587).

For instance, one of the simpler problems is

```julia
function floudas_five(dz_dt, z, phi, t)
    dz_dt[1] = - (2*phi[1] - ((phi[1]*z[2])/((phi[2] + phi[5])*z[1] + z[2])) + phi[3] + phi[4])*z[1]
    dz_dt[2] = ((phi[1]*z[1])*(phi[2]*z[1] - z[2]))/((phi[2] + phi[5])*z[1] + z[2]) + phi[3]*z[1]
    dz_dt[3] = ((phi[1]*z[1])*(z[2] + phi[5]*z[1]))/((phi[2] + phi[5])*z[1] + z[2]) + phi[4]*z[1]
end

```

from this [Handbook of Test Problems (chapter 15.3), Floudas et. al.](https://www.springer.com/us/book/9780792358015).

And I also intend to work towards using with Differential Algebraic Equations as well.

So in the end, in case I’m not mistaken, transforming my problem to the matrix form required by your package might not be the easiest of tasks.

Nonetheless, thanks again for the quick reply!

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