How can we prove that the Schur complement S is positive definite?

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In summary, S is a symmetric positive definite matrix, which is proven by showing that all its eigenvalues are positive. This is demonstrated by assuming that S is not positive definite and arriving at a contradiction, thus proving that S must be positive definite.
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math8
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Let M be a real symmetric and positive definite matrix with blocks A, Bt, B and C.


[tex] M= [[A,B^{t}] ; [B,C]] [/tex]

where [tex] A[/tex] is a [tex] p\times p[/tex] matrix;[tex] B[/tex] is [tex] q\times p[/tex]; and [tex] C[/tex] is [tex] q\times q[/tex].

Let [tex] S=C-BA^{-1}B^{t}[/tex] be the Schur complement. We prove that S is symmetric positive definite.


I can prove that S is symmetric but I am having trouble proving that it is positive definite.
I know that for S a symmetric matrix, S positive definite is equivalent to say that all eigen values of S are positive.

I guess my question is how do we prove that all eigen values of S are positive?
 
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Let us assume that S is not positive definite. Let λ be an eigenvalue of S with corresponding eigenvector x such that Sx=λx. Then, we have (C−BA^{−1}B^{t})x=λx. By multiplying both sides by A^{−1}, we get A^{−1}(C−BA^{−1}B^{t})x=A^{−1}λx. Since A is positive definite, we can further write this equation as (A^{−1}C−B^{t})x=A^{−1}λx. Multiplying both sides by B, we get B(A^{−1}C−B^{t})x=B(A^{−1}λx). By substituting A^{−1}C=M−B^{t}, we have BMx=B(A^{−1}λx). Finally, by multiplying both sides by A, we obtain Mx=Aλx. This shows that λ is an eigenvalue of M which is a contradiction to our assumption that M is positive definite with all its eigenvalues being positive. Therefore, the only possible conclusion is that S must be positive definite.
 

FAQ: How can we prove that the Schur complement S is positive definite?

What is a Schur complement?

The Schur complement is a mathematical concept used in linear algebra and matrix theory. It is defined as the inverse of a specific block of a larger matrix, and is useful for solving systems of linear equations.

How is the Schur complement calculated?

The Schur complement can be calculated using the formula S = A - BD-1C, where A, B, C, and D are the blocks of the original matrix. Alternatively, it can also be calculated using the formula S = A-1 - A-1BCD-1A-1.

What is the significance of the Schur complement?

The Schur complement is significant because it allows for the reduction of a large matrix into smaller blocks, making it easier to solve systems of linear equations. It is also used in optimization problems and in the analysis of control systems.

What are some applications of the Schur complement?

The Schur complement has various applications in fields such as physics, engineering, and economics. It is used in the analysis of systems and networks, in the design of control systems, and in the study of quantum mechanics, among others.

Are there any limitations to using the Schur complement?

One limitation of using the Schur complement is that it is only defined for square matrices. Additionally, it may not always be computationally efficient to calculate the Schur complement, and there are other methods that may be more suitable for solving certain types of systems of linear equations.

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