How Can We Determine Convergence of A^n * b in Linear Algebra?

In summary, the conversation discussed finding the limit as n approaches infinity of A^n * b, where a is a 2x2 matrix and b is a 2x1 vector. The concept of spectral radius was introduced as the largest eigenvalue of A, and if it is less than one, the equation converges to zero. However, the eigenvalues in this case were not less than one, so the question was raised about methods for determining an actual x,y that the system converges to. It was then mentioned that since A is 2x2, it satisfies a second order polynomial, and the characteristic equation can be used to find A^n. Finally, the discussion concluded with the question of how to find what A^n
  • #1
gogetagritz
8
0
I am pretty rusty/unknowledgable when it comes to linear algebra, so when I was given the problem.

Find the limit as n->infinity of A^n * b

where a is a 2x2 matrix and b is a 2x1 vector, I scratched my head.

A fellow student told me about the spectral radius being the largest eigen value of A, and if it is less than one then the equation converges to zero. However my eigenvalues are not less than one. So what methods are there for determing an actual x,y that this this system converges to?
 
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  • #2
since A is 2x2 it satisfies a second order polynomial, the characteristic equation.

X^2+ uX + v=0 for some u,v in R

So, A^n = -uA^{n-1}-vA^{n-2}

for n greater than 2.

if A^nb converges to anything at all can you see how to find what it converges to now?
 
  • #3


There are a few methods for determining the actual values of x and y that the system converges to. One approach is to use the power method, which involves repeatedly multiplying the matrix A by the vector b and normalizing the resulting vector until it converges to the dominant eigenvector. This method can be used if the matrix A has a dominant eigenvalue (the one with the largest absolute value) and the corresponding eigenvector is linearly independent from the other eigenvectors.

Another approach is to use the Jordan canonical form of the matrix A, which can be used to find a general solution for the system of equations. This method can be applied even if the matrix A does not have a dominant eigenvalue.

If the matrix A is diagonalizable, meaning it has a full set of linearly independent eigenvectors, then the solution can be found by diagonalizing the matrix and using the diagonal entries to form a diagonal matrix D. The solution will then be given by the limit of D^n * b as n approaches infinity.

Overall, depending on the specific properties of the matrix A and vector b, there are various methods that can be used to determine the values of x and y that the system converges to. It is important to note that the spectral radius only provides a condition for convergence, and does not necessarily give the exact values of the limit.
 

FAQ: How Can We Determine Convergence of A^n * b in Linear Algebra?

What is the definition of convergence in matrix equations?

Convergence in matrix equations refers to the behavior of a sequence of matrices as the number of iterations approaches infinity. It is the process of finding a solution to a matrix equation that becomes more accurate with each iteration, ultimately reaching a stable and precise solution.

How is convergence determined in matrix equations?

Convergence is determined by monitoring the size of the residual error, which is the difference between the calculated solution and the actual solution. If the residual error decreases with each iteration, the matrix equation is said to be converging.

What are some methods used to improve convergence in matrix equations?

Some methods used to improve convergence in matrix equations include using preconditioners, which are matrices that transform the original matrix equation into a more easily solvable form, and using iterative methods such as Jacobi, Gauss-Seidel, or SOR (Successive Over-Relaxation).

Can a matrix equation fail to converge?

Yes, a matrix equation can fail to converge if the initial guess is too far from the actual solution, or if the matrix is ill-conditioned, meaning it has large differences in the magnitudes of its eigenvalues. In some cases, changing the initial guess or using a different method may help the equation to converge.

What are some applications of convergence in matrix equations?

Convergence in matrix equations has many practical applications, including solving systems of linear equations in engineering, physics, and economics, modeling and predicting the behavior of complex systems, and image and signal processing. It is also used in machine learning and data analysis for tasks such as clustering and dimensionality reduction.

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