Best way to solve a system of complex equations?

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SUMMARY

The discussion focuses on solving a system of complex equations by hand, specifically in the context of circuit analysis. Participants highlight methods such as row reduction, Cramer's rule, and back substitution as viable techniques. It is established that with two equations and three variables, the system is under-determined, meaning a unique solution cannot be achieved. The consensus is that while complex numbers do not complicate the solution process, the absence of a third equation limits the ability to solve for all variables uniquely.

PREREQUISITES
  • Understanding of complex numbers and their properties
  • Familiarity with solving systems of linear equations
  • Knowledge of row reduction and back substitution methods
  • Basic skills in matrix operations
NEXT STEPS
  • Research the application of Cramer's rule for systems of equations
  • Learn about augmented matrices and their role in solving linear systems
  • Study the implications of under-determined systems in linear algebra
  • Explore MATLAB's capabilities for solving complex equations, including eigenvalue calculations
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Students in circuit analysis, mathematicians, and engineers who need to solve systems of complex equations by hand or understand the limitations of under-determined systems.

x86
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In my circuit analysis class I consistently need to solve system of complex equations, and I can't use MATLAB or anything for it. Suppose I have the following system:

(Va-Vs)/(-j15) + Va/33 + (Va-Vo)/(-j25)=0
(Vo-Va)/(-j25) + (Vo-Vs)/10 = 0

What is the best way to solve it by hand in a time convinient manner?
 
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Can't you make into a set of linear equations and then use MATLAB to find the eigenvalues?

(1/33 - 1/j15 - 1/j25) Va + (1/j15) Vs + (1/25) Vo = 0
...

In this case, it seems you're missing a third equation.
 
How do we usually solve systems of linear equations by hand? Row reduction, Cramer's rule, or matrix inversion. Pick whichever one you like best. I don't recommend matrix inversion except for 2x2 systems (since the inverse is "easy" to remember).

jason
 
jedishrfu said:
In this case, it seems you're missing a third equation.

Probably one of the V or maybe of the differences is not an unknown, or else is an input in terms of which it is desired to express everything else.

Given that, if these are considered hard equations I am afraid there is some bad news... :bugeye:
 
jasonRF said:
How do we usually solve systems of linear equations by hand? Row reduction, Cramer's rule, or matrix inversion. Pick whichever one you like best. I don't recommend matrix inversion except for 2x2 systems (since the inverse is "easy" to remember).

jason

I always found row reduction slower than just back-substitution. So essentially if I just do back substitution, I'll get an answer?

Also, i want to express Vo in terms of Vs, so I only have two equations.

If I want to solve for Vo, I'd just solve for Va, then back substitute?

jedishrfu said:
Can't you make into a set of linear equations and then use MATLAB to find the eigenvalues?

(1/33 - 1/j15 - 1/j25) Va + (1/j15) Vs + (1/25) Vo = 0
...

In this case, it seems you're missing a third equation.

Oh I wish, but on tests we have to do it by hand.

jasonRF said:
How do we usually solve systems of linear equations by hand? Row reduction, Cramer's rule, or matrix inversion. Pick whichever one you like best. I don't recommend matrix inversion except for 2x2 systems (since the inverse is "easy" to remember).

jason

I see. I guess complex numbers don't really change anything, even though you'd think it would mean you'd have 2 times more equations to solve
 
jasonRF said:
How do we usually solve systems of linear equations by hand? Row reduction, Cramer's rule, or matrix inversion. Pick whichever one you like best. I don't recommend matrix inversion except for 2x2 systems (since the inverse is "easy" to remember).

x86 said:
I always found row reduction slower than just back-substitution. So essentially if I just do back substitution, I'll get an answer?

Also, i want to express Vo in terms of Vs, so I only have two equations.

If I want to solve for Vo, I'd just solve for Va, then back substitute?
If you're using matrices to solve for the variables, row reduction gets you a matrix with 1's on the diagonal. The 1 at the lower end of the diagonal gives you one of the variables, and you can then use back-substitution to find the values of the other variables. So back-substitution implies that you have already done row reduction, at least in the context of using matrices to solve the system. Otherwise I'm not sure what you're saying.

Also, and as pointed out already by jedishrfu, you have two equations and three variables, so it's not possible to get a unique solution. A system with fewer equations than variables is called under-determined.

jedishrfu said:
Can't you make into a set of linear equations and then use MATLAB to find the eigenvalues?

(1/33 - 1/j15 - 1/j25) Va + (1/j15) Vs + (1/25) Vo = 0
...

In this case, it seems you're missing a third equation.

x86 said:
Oh I wish, but on tests we have to do it by hand.

jasonRF said:
How do we usually solve systems of linear equations by hand? Row reduction, Cramer's rule, or matrix inversion. Pick whichever one you like best. I don't recommend matrix inversion except for 2x2 systems (since the inverse is "easy" to remember).

x86 said:
I see. I guess complex numbers don't really change anything, even though you'd think it would mean you'd have 2 times more equations to solve
No. Each complex solution counts as one solution.

Here's a simple example:
z + w = 2
z - w = 2i

Setting up an augmented matrix:
##\begin{bmatrix} 1 & 1 & | 2\\ 1 & -1 & | 2i \end{bmatrix}##
Using row reduction, we get
##\begin{bmatrix} 1 & 1 & | 2\\ 0 & 1 & | 1 - i \end{bmatrix}##
At this we can note that w = 1 - i, and then back substitute to get z, or we can continue to reduced row-echelon form (RREF) where each row starts with a 1:
##\begin{bmatrix} 1 & 0 & | 1 + i \\ 0 & 1 & | 1 - i \end{bmatrix}##
The single solution is (z, w) = (1 + i, 1 - i).
 
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