Courant fischer min-max theorem

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In summary, the conversation discusses the use of the corollary of the Courant Fischer Min Max theorem in proving a statement involving singular values of a matrix. The corollary states that for a matrix M with singular values \sigma _{1}\geq \cdots \geq \sigma _{q}, the qth singular value, \sigma _{q}, can be written as the minimum of the maximum norms of Mx over a set S of vectors with dimension n-k+1. The conversation then explores using this corollary to prove a statement involving the sum of two matrices A and B and their respective singular values. In particular, the statement being proven is \sigma _{i+j-1}(A
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Assuming the corollary of the Courant Fischer Min Max theorem,

[tex]\sigma _{1}\geq \cdots \geq \sigma _{q} [/tex] are the singular values of a matrix [tex]M \in \textbf{C}^{m \times n}[/tex] in decreasing order, q = min {m,n}

[tex]\sigma _{k}(M)= min_{S} max \left\{\left\| Mx\right\| _{2}: x\in S, \left\| x\right\| _{2} =1 \right\} [/tex] , where dim(S)= n-k+1, [tex] S \in \textbf{C}^{n}[/tex].

we want to prove that

[tex]\sigma _{i+j-1}(A+B)\leq \sigma _{i}(A) + \sigma _{j}(B) [/tex] for all [tex]i,j=1,2,..., q[/tex] and [tex]i+j \leq q[/tex].

I have tried to do this using i+j-1 instead of k, and A+B instead of M, I am getting

[tex]\sigma _{i+j-1}(A+B) \leq min_{S} max \left\{ (\left\| Ax\right\| _{2} +\left\| Bx\right\| _{2}): x\in S, \left\| x\right\| _{2} =1 \right\} [/tex] , where dim(S)= n- (i+j-1) +1.
But from here, I am not sure if I can distribute the min max to get [tex]min max (\left\| Ax\right\| _{2} )+ min max (\left\| Bx\right\| _{2})[/tex], which in that case would give me the desired result since the singular values are in decreasing order.

For some reason, I am doubting that .
 
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Is it possible to prove this result using the corollary of the Courant Fischer Min Max theorem? How can I do it? Thank you.
 

1. What is the Courant-Fischer min-max theorem?

The Courant-Fischer min-max theorem is a fundamental theorem in mathematical analysis that relates the eigenvalues of a Hermitian matrix to certain optimization problems. It provides a way to find the largest and smallest eigenvalues of a matrix by optimizing over certain subspaces.

2. What are the applications of the Courant-Fischer min-max theorem?

The Courant-Fischer min-max theorem has many applications in various fields such as quantum mechanics, statistical mechanics, and optimization. It is used in the analysis of quantum systems, spectral graph theory, and in finding optimal solutions to various optimization problems.

3. What is a Hermitian matrix?

A Hermitian matrix is a square matrix that is equal to its own conjugate transpose. In other words, it is a complex matrix that is equal to its own conjugate when its rows and columns are interchanged.

4. How does the Courant-Fischer min-max theorem relate to the Rayleigh quotient?

The Courant-Fischer min-max theorem is closely related to the Rayleigh quotient, which is a way to define the eigenvalues of a matrix. The theorem provides a way to find the maximum and minimum values of the Rayleigh quotient over certain subspaces, which in turn gives the largest and smallest eigenvalues of the matrix.

5. Can the Courant-Fischer min-max theorem be extended to non-Hermitian matrices?

Yes, the Courant-Fischer min-max theorem has been extended to non-Hermitian matrices in various ways. One approach is to use the generalized eigenvalue problem, which allows for the study of non-Hermitian matrices with complex eigenvalues. Another approach is to use the field of functional analysis to study non-Hermitian matrices and their eigenvalues.

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