Solve system of equations

[Askic]

I see,
this looks like as if the constrained optimization would probably do better (since L,C and R cannot be negative)

import math
from scipy.optimize import least_squares


def f(LCR):
    # variables
    L = LCR[0]
    Cs = LCR[1]
    Rs = LCR[2]

    # global constants
    Pi = math.pi
    Rin = 10.0
    Cl = 3.0e-12

    # measurement-related constants
    f0 = 130e6
    f1 = 140e6
    f2 = 150e6
    omg = [0, 0, 0]
    out = [0, 0, 0]
    omg[0] = 2*Pi*f0
    omg[1] = 2*Pi*f1
    omg[2] = 2*Pi*f2
    out[0] = 6.658482971121064
    out[1] = 25.89226207742419
    out[2] = 7.013950018010094

    # A and B arrays
    Ar = [0, 0, 0]
    Aj = [0, 0, 0]
    Br = [0, 0, 0]
    Bj = [0, 0, 0]
    equ = [0, 0, 0]

    # equation building
    for i in range(3):
        Ar[i] = 1 - omg[i]**2*Cl*L
        Aj[i] = omg[i]*Cs*Rs
        Br[i] = 1 - omg[i]**2*(L*(Cs + Cl) + Cl*Cs*Rin*Rs)
        Bj[i] = omg[i]*(Cs*Rs + Cl*Rin + Cl*Rs - omg[i]**2*Cl*Cs*Rin*L)

        equ[i] = out[i] - math.sqrt(Ar[i]*Ar[i] + Aj[i]
                                    * Aj[i])/math.sqrt(Br[i]*Br[i] + Bj[i]*Bj[i])
    return equ


LCR0 = [0.3e-6, 1e-12, 1]
res = least_squares(f, (LCR0), bounds=((0, 0, 0), (1.0e-6, 1.0e-10, 2)))
print(res)

Let me know if this is closer to the expected solution.