Determinant and symmetric positive definite matrix

In summary, the lecture notes state that for a matrix to be positive definite, its determinant must be positive. This is because if the matrix is symmetric, it is only positive definite if it has positive eigenvalues. However, the earlier given matrix S is positive definite regardless of the value of d. By calculating the eigenvalues, it is shown that the smallest eigenvalue is always positive, resulting in a positive determinant.
  • #1
Incand
334
47
As a step in a solution to another question our lecture notes claim that the matrix (a,b,c,d are real scalars).

\begin{bmatrix}
2a & b(1+d) \\
b(1+d)& 2dc \\
\end{bmatrix}

Is positive definite if the determinant is positive. Why? Since the matrix is symmetric it's positive definite if the it got positive (real) eigenvalues and ##det (A) = \prod \lambda_i## but two negative eigenvalues would give a positive determinant too.

Earlier in the text its given that the matrix
##
S =
\begin{bmatrix}
a & b \\
b& c \\
\end{bmatrix}
##
is positive definitive while ##d## is without any restrictions if that is somehow relevant.
 
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  • #2
Edit: nevermind I solved it

If i calculate the eigenvalues I get
##0 = (2a-\lambda )(2dc-\lambda) - b^2(1+d)^2 = 4adc +\lambda^2 - (2a+2dc)\lambda - b^2(1+d)^2 = det(A) + \lambda^2 -(2a+2dc)\lambda##
then
## \lambda^2-(2a+2dc)\lambda < 0## since ##det(A) > 0##
equal when
##(\lambda-(a+dc))^2-(a+dc)^2 = 0 \Longleftrightarrow \lambda = (a+dc) \pm (a+dc)##
so the smallest eigenvalue is always ##\lambda> 0 ##.
 
Last edited:

1. What is a determinant and how is it calculated for a matrix?

A determinant is a numerical value that can be calculated for a square matrix. It represents the scaling factor of the transformation that the matrix performs. The determinant of a matrix can be calculated using various methods, such as cofactor expansion or using row operations.

2. What is a symmetric positive definite matrix?

A symmetric positive definite matrix is a square matrix where all the elements are real numbers and the matrix is equal to its own transpose. Additionally, all the eigenvalues of a symmetric positive definite matrix are positive. This type of matrix has many useful properties, such as being invertible and having a unique Cholesky decomposition.

3. How can I determine if a matrix is symmetric positive definite?

To determine if a matrix is symmetric positive definite, you can check if it meets the following criteria: all the elements are real numbers, the matrix is equal to its own transpose, and all the eigenvalues are positive. Additionally, you can use the Cholesky decomposition method to check if a matrix is symmetric positive definite.

4. What are the applications of symmetric positive definite matrices in science?

Symmetric positive definite matrices have many applications in science and mathematics. They are commonly used in optimization problems, such as in machine learning algorithms, where they represent the covariance matrix. They are also used in finite element analysis, signal processing, and statistics.

5. Can a non-square matrix have a determinant or be symmetric positive definite?

No, a non-square matrix does not have a determinant or a symmetric positive definite property. Determinants are only defined for square matrices, and the symmetry property requires the matrix to be equal to its own transpose, which is not possible for a non-square matrix.

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