Yeah, It's linear_eq_to_matrix in sympy.solvers.solveset.
Example:
>>> eqns = [x + 2*y + 3*z - 1, 3*x + y + z + 6, 2*x + 4*y + 9*z - 2]
>>> A, b = linear_eq_to_matrix(eqns, [x, y, z])
>>> A
Matrix([
[1, 2, 3],
[3, 1, 1],
[2, 4, 9]])
>>> b
Matrix([
[ 1],
[-6],
[ 2]])
AMiT Kumar
On Wednesday, October 7, 2015 at 9:23:02 PM UTC+5:30, Jason Moore wrote:
>
> We've just introduced a function called linsolve from the GSoC work this
> summer. It has the capability to do this.
>
>
> Jason
> moorepants.info
> +01 530-601-9791
>
> On Wed, Oct 7, 2015 at 8:25 AM, Adam Leeper <[email protected]
> <javascript:>> wrote:
>
>> Hi all-
>>
>> Interested party just wondering if there is any update on this.
>>
>> Cheers,
>> Adam
>>
>> On Tuesday, June 24, 2014 at 10:01:56 AM UTC-7, Aaron Meurer wrote:
>>>
>>> A multidimensional version of collect() would probably be the best
>>> abstraction.
>>>
>>> Aaron Meurer
>>>
>>> On Sun, Jun 15, 2014 at 10:26 AM, James Crist <[email protected]> wrote:
>>> > We certainly could. The question would then be what the scope of the
>>> method
>>> > should be. Should it only handle systems that can be expressed as Ax =
>>> b? Or
>>> > should it behave like `CoefficientArrays` mentioned above, and handle
>>> Ax +
>>> > Bx^2 + Cx^3 + D = 0? Either way, I think it should error if the form
>>> can't
>>> > be matched exactly (i.e. don't linearize, just express a linear, or
>>> > polynomial, system as matrices).
>>> >
>>> >
>>> > On Saturday, June 14, 2014 6:44:21 PM UTC-5, Aaron Meurer wrote:
>>> >>
>>> >> Oh, of course. B is on the rhs. This is probably more natural to me
>>> too.
>>> >>
>>> >> Should we make a convenience function that does this? I think this
>>> use
>>> >> of jacobian would be lost on most people.
>>> >>
>>> >> Aaron Meurer
>>> >>
>>> >> On Sat, Jun 14, 2014 at 6:29 PM, James Crist <[email protected]>
>>> wrote:
>>> >> > It's just the convention I'm most used to. Systems that can be
>>> expressed
>>> >> > as
>>> >> > A*x = B I usually solve for x, or if A isn't square, the least
>>> squares
>>> >> > solution x. In both cases you need A and B in this form. I suppose
>>> Ax +
>>> >> > B
>>> >> > could seem more natural though.
>>> >> >
>>> >> > On Friday, June 13, 2014 6:45:48 PM UTC-5, Aaron Meurer wrote:
>>> >> >>
>>> >> >> That's a clever trick. I should have thought of that.
>>> >> >>
>>> >> >> Is there any reason you let system = A*x - B instead of A*x + B?
>>> The
>>> >> >> latter seems more natural.
>>> >> >>
>>> >> >> Aaron Meurer
>>> >> >>
>>> >> >> On Sat, Jun 7, 2014 at 12:28 AM, James Crist <[email protected]>
>>> wrote:
>>> >> >> > I just answered this on gitter earlier today, but you can just
>>> take
>>> >> >> > the
>>> >> >> > jacobian of the system to get its matrix form. For example:
>>> >> >> >
>>> >> >> > In [1]: from sympy import *
>>> >> >> >
>>> >> >> > In [2]: a, b, c, d = symbols('a, b, c, d')
>>> >> >> >
>>> >> >> > In [3]: x1, x2, x3, x4 = symbols('x1:5')
>>> >> >> >
>>> >> >> > In [4]: x = Matrix([x1, x2, x3, x4])
>>> >> >> >
>>> >> >> > In [5]: system = Matrix([a*x1 + b*x2 + c,
>>> >> >> > ...: c*x1 + d*x3 + 2,
>>> >> >> > ...: c*x3 + b*x4 + a])
>>> >> >> >
>>> >> >> > In [6]: A = system.jacobian(x)
>>> >> >> >
>>> >> >> > In [7]: B = A*x - system
>>> >> >> >
>>> >> >> > In [8]: A
>>> >> >> > Out[8]:
>>> >> >> > Matrix([
>>> >> >> > [a, b, 0, 0],
>>> >> >> > [c, 0, d, 0],
>>> >> >> > [0, 0, c, b]])
>>> >> >> >
>>> >> >> > In [9]: B
>>> >> >> > Out[9]:
>>> >> >> > Matrix([
>>> >> >> > [-c],
>>> >> >> > [-2],
>>> >> >> > [-a]])
>>> >> >> >
>>> >> >> > In [10]: assert A*x - B == system
>>> >> >> >
>>> >> >> > The functionality I'm adding for my GSoC for linearizing a
>>> system of
>>> >> >> > equations will also be able to return these matrices in a
>>> convenient
>>> >> >> > form.
>>> >> >> > But it's not terribly difficult to solve for these arrangements
>>> using
>>> >> >> > the
>>> >> >> > current functionality.
>>> >> >> >
>>> >> >> >
>>> >> >> >
>>> >> >> >
>>> >> >> >
>>> >> >> >
>>> >> >> >
>>> >> >> > On Thursday, June 5, 2014 4:22:52 PM UTC-5, Andrei Berceanu
>>> wrote:
>>> >> >> >>
>>> >> >> >> Was this implemented into sympy at any point? It could be the
>>> >> >> >> equivalent
>>> >> >> >> of Mathematica's CoefficientArrays function.
>>> >> >> >>
>>> >> >> >> On Thursday, November 14, 2013 5:56:22 AM UTC+1, Chris Smith
>>> wrote:
>>> >> >> >>>
>>> >> >> >>> I forgot that as_independent, without the as_Add=True flag
>>> will
>>> >> >> >>> treat
>>> >> >> >>> Muls differently. The following will be more robust:
>>> >> >> >>>
>>> >> >> >>> def eqs2matrix(eqs, syms, augment=False):
>>> >> >> >>> """
>>> >> >> >>> >>> s
>>> >> >> >>> [x + 2*y == 4, 2*c + y/2 == 0]
>>> >> >> >>> >>> eqs2matrix(s, (x, c))
>>> >> >> >>> (Matrix([
>>> >> >> >>> [1, 0],
>>> >> >> >>> [0, 2]]), Matrix([
>>> >> >> >>> [-2*y + 4],
>>> >> >> >>> [ -y/2]]))
>>> >> >> >>> >>> eqs2matrix([2*c*(x+y)-4],(x, y))
>>> >> >> >>> (Matrix([[2*c, 2*c]]), Matrix([[4]]))
>>> >> >> >>> """
>>> >> >> >>> s = Matrix([si.lhs - si.rhs if isinstance(si, Equality)
>>> else si
>>> >> >> >>> for
>>> >> >> >>> si in eqs])
>>> >> >> >>> sym = syms
>>> >> >> >>> j = s.jacobian(sym)
>>> >> >> >>> rhs = -(s - j*Matrix(sym))
>>> >> >> >>> rhs.simplify()
>>> >> >> >>> if augment:
>>> >> >> >>> j.col_insert(0, rhs)
>>> >> >> >>> else:
>>> >> >> >>> j = (j, rhs)
>>> >> >> >>> return j
>>> >> >> >>>
>>> >> >> > --
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