How to Use Cholesky Factorization for Positive Definite Matrices

In summary, the conversation discusses the use of Cholesky factorization to test for positive semidefinite matrices. It is a simpler method compared to using determinants and works for both positive definite and semidefinite matrices. The conversation also mentions using this method to solve part b of the homework problem, which involves showing that f1 is a single perfect square. Part c of the homework problem has not been addressed yet.
  • #1
freshnitesh
1
0

Homework Statement



0dVsrxc.png


2. The attempt at a solution

So part a. makes sense to me, it basically comes down to

A1 =
1 -1 -1
-1 1 1
-1 1 1

A2 =
1 -1 -1
-1 2 -2
-1 -2 11

I'm not sure how to approach part b. because the question doesn't make much sense to me. It's asking to show that f1 is a single perfect square, so my guess is that from A1 I have to derive an expression for f1 that is a single perfect square.

Haven't really looked to hard at part c, yet.
 
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  • #2
freshnitesh said:

Homework Statement



0dVsrxc.png


2. The attempt at a solution

So part a. makes sense to me, it basically comes down to

A1 =
1 -1 -1
-1 1 1
-1 1 1

A2 =
1 -1 -1
-1 2 -2
-1 -2 11

I'm not sure how to approach part b. because the question doesn't make much sense to me. It's asking to show that f1 is a single perfect square, so my guess is that from A1 I have to derive an expression for f1 that is a single perfect square.

Haven't really looked to hard at part c, yet.

Use Chpolesky factorization, which works for any positive-semidefinite matrix. (Many discussions assume positive definiteness, but if you go through the material carefully you can show that it applies as well to the semidefinite case.) See, eg., http://en.wikipedia.org/wiki/Cholesky_decomposition .
In fact, using the Cholesky algorithm is by far the easiest way to test positive definitness or semidefinitness---much easier than dealing with determinants, for example.
 

1. What is a positive definite matrix?

A positive definite matrix is a square matrix where all the eigenvalues are positive. This means that for any non-zero vector, the result of multiplying it by the matrix will always be a positive number.

2. What are some properties of positive definite matrices?

Some properties of positive definite matrices include:

  • All of its diagonal elements are positive.
  • It is invertible.
  • The determinant is positive.
  • All of its principal minors are positive.
  • It is symmetric.

3. How do you determine if a matrix is positive definite?

To determine if a matrix is positive definite, you can use the following methods:

  • Check if all eigenvalues are positive.
  • Use the Cholesky decomposition method.
  • Check if all principal minors are positive.
  • Use the Sylvester's criterion.

4. What is the significance of positive definite matrices in linear algebra?

Positive definite matrices are important in linear algebra because they have several useful properties that make them useful in applications such as optimization, statistics, and machine learning. They also have a close relationship with quadratic forms and can be used to define inner product spaces.

5. What are some examples of real-life applications of positive definite matrices?

Positive definite matrices have various real-life applications, including:

  • In statistics, they are used to define covariance matrices in multivariate analysis.
  • In machine learning, they are used in algorithms such as Principal Component Analysis and Support Vector Machines.
  • In optimization, they are used to find the minimum of a quadratic function.
  • In physics, they are used to define the kinetic energy of a system.

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