Conditions on negative definiteness

In summary: I don't think so.No, it won't help really. The matrix inequality is already linear (LMI) and Schur complement in that sense will just cause a second problem that the off-diagonal terms will cause nonlinearty (later ##R## should be found by interior point method). I am expecting the answer to be in the form of a second LMI on ##R## but the problem is that a lot of simplifications or even assumptions can not be done since ##R## is not square.
  • #1
p4wp4w
8
5
Hello, I want to know if there exist any result in literature that answers my question:
Under which conditions on the real valued matrix ## R ## (symmetric positive definite), the first argument results in and guarantees the second one:
1) for real valued matrices ##A, B, C,## and ## D ## with appropriate dimensions and ## A ## and ## D ## being symmetric:
##X=
\begin{pmatrix}
A & B+RC\\
B^T+C^TR & D\\
\end{pmatrix} < 0##
2)
##
Y
=
\begin{pmatrix}
A & B+C\\
B^T+C^T & D\\
\end{pmatrix} < 0##
Congruence transformation doesn't help since it will affect the diagonal elements as well.
Thank you all in advance.
 
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  • #2
Have you tried looking at Schur complements? I'm not sure it would help, but maybe.
 
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  • #3
perplexabot said:
Have you tried looking at Schur complements? I'm not sure it would help, but maybe.
No, it won't help really. The matrix inequality is already linear (LMI) and Schur complement in that sense will just cause a second problem that the off-diagonal terms will cause nonlinearty (later ##R## should be found by interior point method). I am expecting the answer to be in the form of a second LMI on ##R## but the problem is that a lot of simplifications or even assumptions can not be done since ##R## is not square.
 
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  • #4
p4wp4w said:
No, it won't help really. The matrix inequality is already linear (LMI) and Schur complement in that sense will just cause a second problem that the off-diagonal terms will cause nonlinearty (later ##R## should be found by interior point method). I am expecting the answer to be in the form of a second LMI on ##R## but the problem is that a lot of simplifications or even assumptions can not be done since ##R## is not square.
Hmmm. Maybe you are right, Schur may not be of use. I was just throwing things out there.

Wait, you say in your last post that [itex]R[/itex] is not square but in your original post you say it is positive definite?! Which one is it?
If [itex]R[/itex] is positive definite and if you assume the first argument (1) is true, then one condition on [itex]R[/itex] that results in argument (2) being satisfied is if [itex]R[/itex] is simply the Identity matrix : P
 
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  • #5
perplexabot said:
Hmmm. Maybe you are right, Schur may not be of use. I was just throwing things out there.

Wait, you say in your last post that [itex]R[/itex] is not square but in your original post you say it is positive definite?! Which one is it?
If [itex]R[/itex] is positive definite and if you assume the first argument (1) is true, then one condition on [itex]R[/itex] that results in argument (2) being satisfied is if [itex]R[/itex] is simply the Identity matrix : P
That's a nasty mistake that I made; I think deep down, I was looking for something like SPDness of ##R## to simplify things but ##R## is not square in general.
 
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  • #6
p4wp4w said:
That's a nasty mistake that I made; I think deep down, I was looking for something like SPDness of ##R## to simplify things but ##R## is not square in general.
Can you assume ##A## or ##D## to be positive definite?
 

1. What is negative definiteness?

Negative definiteness is a mathematical property of a matrix or quadratic form, where all eigenvalues of the matrix or all values of the quadratic form are negative.

2. How is negative definiteness related to positive definiteness?

Negative definiteness is the opposite of positive definiteness. While positive definiteness means all eigenvalues or values are positive, negative definiteness means they are all negative. Both properties are important in determining the nature of a matrix or quadratic form.

3. What are the implications of a matrix or quadratic form being negative definite?

A negative definite matrix or quadratic form has several implications, including the existence of a unique minimum value, the convexity of the function it represents, and its use in optimization problems.

4. How is negative definiteness tested?

Negative definiteness can be tested using several methods, such as the Sylvester's criterion, the leading principal minors test, and the eigenvalue test. These methods involve checking the signs of the eigenvalues or principal minors of the matrix or quadratic form.

5. What are some applications of negative definiteness?

Negative definiteness has various applications in mathematics, physics, and engineering. It is used in optimization problems, stability analysis of dynamical systems, and the study of elliptic partial differential equations, among others.

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