if you define k_delta*k_phi as another terms, e.g. c4, 
then k_phi disappears and the equations are linear.

On Tuesday, July 26, 2016 at 11:12:50 AM UTC-5, Jason Moore wrote:
>
> I have been working on a problem and end up needing to solve a system of 
> nonlinear equations that are polynomials wrt to the variables of interest. 
> There are six equations and six unknowns and only two of the variables are 
> related non-linearly. This is the basic form:
>
> [-c3 + 55.3719398861938],
> [-c2 - 3*c3 + 1524.09472216942],
> [-c1 - 3*c2 - 100*c3 - 111.682822870417*k_delta*k_phi + 16670.819288228]
> [-c0 - 3*c1 - 100*c2 - 8153.56338281556*k_delta*k_phi + 
> 3891.45616272381*k_delta + 55610.0242428841]
> [-3*c0 - 100*c1 - 90978.6714602982*k_delta*k_phi + 276475.776481344]
> [-100*c0 - 38237.9055133813*k_delta + 84011.44228978]
>
> But more generally, I have symbolic coefficients. SymPy solve solves this 
> correctly! Which is cool, but it took 4 hours to solve on my machine. I 
> haven't tried to solve the above numerical form yet.
>
> I'm curious if there is something I should be doing with SymPy to help it 
> solve this faster. If I change some of the symbols to specific rationals 
> I've had it solve as quickly as 20 minutes.
>
> The full example is here: 
> http://nbviewer.jupyter.org/gist/moorepants/0e85508f1b0753b01e4d9ced83e3519c/solve_inner_loop_gains.ipynb
>
> Jason
> moorepants.info
> +01 530-601-9791
>

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