Symmetric positive definite matrix

In summary, if A is symmetric positive semi-definite, there exists a symmetric matrix B such that A=B^2, where B is defined as B=Q\sqrt{\Lambda} Q^T. This same method can also be applied for finding a matrix B when A is symmetric positive definite.
  • #1
IniquiTrance
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Homework Statement


(i) Show that if [itex]A[/itex] is symmetric positive semi-definite, then there exists a symmetric matrix [itex]B[/itex] such that [itex]A=B^2[/itex].

(ii) Let [itex]A[/itex] be symmetric positive definite. Find a matrix [itex]B[/itex] such that [itex]A=B^2[/itex].

Homework Equations


The Attempt at a Solution



For part 1, I used:

[tex] B = Q\sqrt{\Lambda} Q^T[/tex]

So that,

[tex]B^T B = (Q\sqrt{\Lambda} Q^T)^T Q\sqrt{\Lambda} Q^T[/tex]
[tex]=Q\sqrt{\Lambda} Q^T Q\sqrt{\Lambda} Q^T[/tex]
[tex]=Q\Lambda Q^T[/tex]
[tex]=A[/tex]

I am assuming this cannot be used for part 2. Any help is much appreciated!
 
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  • #2
Why should it be different?

http://en.wikipedia.org/wiki/Positive-definite_matrix

semi positive means non-negative and positive is positive, this means that the eigenvalues are either way ≥0, so you can define a square root of a diagonal matrix in both cases as the square roots of the eigenvalues.
 
  • #3
MathematicalPhysicist said:
Why should it be different?

http://en.wikipedia.org/wiki/Positive-definite_matrix

semi positive means non-negative and positive is positive, this means that the eigenvalues are either way ≥0, so you can define a square root of a diagonal matrix in both cases as the square roots of the eigenvalues.

Thanks.
 

FAQ: Symmetric positive definite matrix

What is a symmetric positive definite matrix?

A symmetric positive definite matrix is a square matrix where all of its elements are real numbers and its transpose is equal to itself. Additionally, all of its eigenvalues are positive, making it a special type of matrix that has many useful properties in linear algebra and optimization.

How can I tell if a matrix is symmetric positive definite?

To determine if a matrix is symmetric positive definite, you can check if it is symmetric first by comparing it to its transpose. Then, you can use the Cholesky decomposition or the eigenvalues of the matrix to check if they are all positive. If both of these conditions are met, then the matrix is symmetric positive definite.

What are the applications of symmetric positive definite matrices?

Symmetric positive definite matrices have many applications in various fields of science and technology. They are commonly used in optimization problems, such as in machine learning algorithms and computer graphics. They also have applications in the study of partial differential equations and in physics, particularly in quantum mechanics.

What is the importance of positive definiteness in a matrix?

Positive definiteness is important in a matrix because it ensures that the matrix has certain desirable properties, such as being invertible and having a unique solution to a linear system of equations. It also guarantees that the matrix will have real and positive eigenvalues, which are useful in many applications.

Can a matrix be symmetric positive definite if it is not square?

No, a matrix must be square to be considered symmetric positive definite. This is because the definition of a symmetric matrix requires it to be square, and the requirement for all eigenvalues to be positive also applies to square matrices only. However, non-square matrices can still be symmetric and positive definite along their square submatrices.

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