Thank you. How nice an simple.

Sounds like a strategy that could be baked into solve().


Jason
moorepants.info
+01 530-601-9791

On Tue, Jul 26, 2016 at 2:48 PM, Denis Akhiyarov <denis.akhiya...@gmail.com>
wrote:

> 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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