The Rayleigh quotient iteration

In summary: Therefore, S is a set of measure 0. In summary, the Rayleigh quotient iteration is being applied to a matrix A with initial guesses in a subset S of R^2. This subset consists of vectors whose entries are both 0 or both non-zero, and is a measure 0 set. The iteration process involves computing the Rayleigh quotient at each step, starting with an initial guess of an eigenvalue and its corresponding eigenvector. However, if the initial guess satisfies the condition of both entries being 0 or both non-zero, the iteration will not converge.
  • #1
math8
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I am trying to apply the Rayleigh quotient iteration to the matrix A=

a 0
0 b

where a is different from b (a and b are reals).

Find the subsets S of R^2(Reals^2) having the property that the iteration applied to this matrix with initial guesses in S do not converge. Is S a set of measure 0?

I know the Rayleigh quotient is R(A,x)=x*Ax/x*x for A Hermitian and x nonzero. I am supposed to begin with an eigenvalue guess mo and an eigenvector guess vo and use the R.Q. but I am not sure which guesses should I begin with.

I am not sure how to proceed here. Any help is much appreciated.
 
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  • #2
The set S of initial guesses which do not converge is the set of vectors whose entries are both 0 or both non-zero. This set is a measure 0 set, as it has zero area in the plane. To apply the Rayleigh quotient iteration for this matrix, we start with an initial guess (m_0, v_0) such that m_0 is a guessed eigenvalue and v_0 is a corresponding guessed eigenvector. We then iterate by computing the Rayleigh quotient at each step: m_(k+1) = v_k*A*v_k/(v_k*v_k) v_(k+1) = A*v_k/|A*v_k|We then repeat this process until convergence. Note that if the initial guess (m_0, v_0) satisfies the condition that both entries of v_0 are either 0 or both non-zero, then this iteration will not converge, since the magnitude of A*v_k will always be 0.
 

Related to The Rayleigh quotient iteration

1. What is the Rayleigh quotient iteration?

The Rayleigh quotient iteration is a method used to approximate the eigenvalues and eigenvectors of a given matrix. It is an iterative process that repeatedly applies the Rayleigh quotient to converge on the desired eigenvalues and eigenvectors.

2. How does the Rayleigh quotient iteration work?

The Rayleigh quotient iteration works by using the Rayleigh quotient, which is a function that calculates the ratio of the dot product of a vector with a matrix to the dot product of the vector with itself. This ratio is then used to update the vector and improve the approximation of the eigenvalue and eigenvector.

3. What are the advantages of using the Rayleigh quotient iteration?

The Rayleigh quotient iteration has several advantages, including its ability to converge quickly to the desired eigenvalues and eigenvectors. It also does not require any prior knowledge of the eigenvalues and eigenvectors, making it useful for a wide range of matrices.

4. What are the limitations of the Rayleigh quotient iteration?

One limitation of the Rayleigh quotient iteration is that it may not always converge to the desired eigenvalues and eigenvectors. This can happen when the matrix is ill-conditioned or has multiple eigenvalues with similar magnitudes. Another limitation is that it can be computationally expensive for large matrices.

5. How is the Rayleigh quotient iteration used in real-world applications?

The Rayleigh quotient iteration has various applications in fields such as physics, engineering, and finance. It is commonly used in quantum mechanics to calculate the energy levels of a system, in structural analysis for determining natural frequencies, and in finance for analyzing the risk and return of investment portfolios.

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