Systems of Linear Homogenous Differential equations with Constant Coefficients

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SUMMARY

This discussion focuses on solving Systems of Linear Homogenous Differential Equations with Constant Coefficients, specifically represented as \(\frac{dX}{dt} = AX\), where \(A\) is a matrix and \(X\) is a vector. The solution is expressed as \(X = \xi e^{\lambda t}\), with \(\xi\) being the eigenvectors and \(\lambda\) the eigenvalues of \(A\). The challenge presented involves understanding the coupling of variables in the system, particularly when \(n=2\), and how to incorporate the transfer matrix to achieve the desired mixing of components in the solution.

PREREQUISITES
  • Understanding of Linear Algebra, specifically eigenvalues and eigenvectors.
  • Familiarity with Differential Equations, particularly homogeneous systems.
  • Knowledge of matrix diagonalization and transformation techniques.
  • Experience with mathematical notation and concepts related to systems of equations.
NEXT STEPS
  • Study the method of diagonalization for matrices, focusing on conditions for diagonalizability.
  • Learn about the transfer matrix technique in the context of coupled differential equations.
  • Explore alternative solution methods for linear differential equations, such as the Laplace transform.
  • Investigate the implications of time independence in systems of differential equations.
USEFUL FOR

This discussion is beneficial for mathematicians, engineers, and students studying systems of differential equations, particularly those interested in the coupling of variables and advanced solution techniques in linear algebra.

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Hello,
I am looking at different ways to solve Systems of Linear Homogenous Differential equations with Constant Coefficients that is \acute{x}=Ax (x and x' are vectors A is a matix) then the solutions are x= \xi e^{\lambda t} where \xi are the eigenvectors and \lambda the eigenvalues of A and the general solution is the sum of all the eigenvectors with constants inserted.
i.e x= c_{1}\xi^{(1)} e^{\lambda t}+c_{2}\xi^{(2)} e^{\lambda t}

The problem I have with this is that I can't figure out how to get mixing since surely if n=2, say, then x=(x1,x2) but to get x1 you are just adding weighted amounts of x1 doesn't ever couple to x2 i.e x_{1} = c_{1}\xi^{(1)}_1 e^{\lambda t}+c_{2}\xi^{(2)}_{1} e^{\lambda t}I assume I am being idiot but if someone could point out where I am going wrong that would be brilliant.

Thanks very much,

P.S I normally solve it like this http://physics.ucsc.edu/~peter/114A/coupled_fol.pdf if you know any links to other ways to solve them I would be grateful.
 
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If you have :
d X/dt = AX where A is a diagonalisable matrix and X a column vector.
U^-1AU=D where D is the diagonal matrix : diag(lambda_1...lambda_n)

d X/dt = AX <=>
d X/dt = UDU^-1X <=>
U^-1dX/dt = DU^-1X <=>
dY/dt = D Y with Y =U^-1X provided that A is time independent. The mixing comes from the fact the eigendirections are Y and not X : you need the transfer matrix.
 

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