Need Help with positive definite matrices

In summary, to show that A is equal to C C^T, where C has orthogonal columns, one can use the fact that A is positive definite and symmetric. Using an orthogonal matrix P, it can be shown that A is equal to the diagonal matrix D. By defining B as the diagonal matrix with the square root of the diagonal entries of D, and C as P^TB P, it can be shown that C C^T is equal to A. This is because PDP^T = A, and C has orthogonal columns since its transpose multiplied by itself results in a diagonal matrix.
  • #1
Basil4000
3
0

Homework Statement


If A is positive definite, show that ## A = C C^T ## where ## C ## has orthogonal columns.

The Attempt at a Solution



So, I've got the first part figured out. Because ## A ## is symmetric, an orthogonal matrix ## P ## exists such that ## P^TA P = D = diag(\lambda_1,...,\lambda_n) ## where ## \lambda_i > 0 ## because A is positive definite. Next I've defined ## B = diag(\sqrt{\lambda_1},...,\sqrt{\lambda_n}) ## Then I wrote ## C = P^TB P ## then ## C C^T = (P^T B P) (P^T B P)^T = (P^T B P) P^T B^T P = P^T B B P = P^T D P = A ##

So I'm at the last step and I'm stuck on how to show that C has orthogonal columns. Any hints?
 
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  • #2
C has orthogonal columns if and only if CtC is a diagonal matrix (you should be able to check that this claim is true easily- the off diagonal entries of Ct C are the inner products of the columns of C).

Also PDPt = A, not PtDP.
 
  • #3
Right, that's just sloppyness on my part typing that out. I haven't learned anything about inner products yet. Is there a way to look at this differently?
 
  • #4
Inner product is the same as dot product. The typical definition of orthogonal vectors is that their dot product is zero, if you're working with a different definition you'll have to say what it is
 
  • #5
I just read your first reply again. I get it now. I was pretty tired last night. Thanks a lot!
 

What is a positive definite matrix?

A positive definite matrix is a square matrix where all the eigenvalues are positive. In other words, for any vector x, the product xTAx is always positive.

Why are positive definite matrices important?

Positive definite matrices are important because they have many useful properties in linear algebra, optimization, and statistics. They are used in a variety of applications, such as solving systems of linear equations, finding minimum and maximum values of functions, and performing data analysis.

How do you determine if a matrix is positive definite?

To determine if a matrix is positive definite, you can use the Cholesky decomposition method or the Sylvester's criterion. The Cholesky method involves decomposing the matrix into a lower triangular matrix and its transpose, and checking if all the diagonal elements of the lower triangular matrix are positive. Sylvester's criterion states that a matrix is positive definite if and only if all its leading principal minors are positive.

Can a matrix be both positive definite and positive semidefinite?

No, a matrix cannot be both positive definite and positive semidefinite. A matrix is positive semidefinite if all its eigenvalues are non-negative, while a matrix is positive definite if all its eigenvalues are positive. Therefore, a positive definite matrix is a subset of positive semidefinite matrices.

What are the real-world applications of positive definite matrices?

Positive definite matrices have various real-world applications, such as in machine learning, image processing, and signal processing. They are also used in solving optimization problems, such as finding the minimum or maximum value of a function. Additionally, they are used in physics and engineering to model and analyze systems with multiple variables.

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