Help understanding the definition of positive semidefinite matrix

In summary, a positive semidefinite matrix is a symmetric matrix whose eigenvalues are all non-negative. This means that for any non-zero vector \( x \), the quadratic form \( x^T A x \) is greater than or equal to zero, indicating that the matrix does not produce negative outputs when applied in this manner. Positive semidefinite matrices are important in various fields, including optimization and statistics, as they ensure certain properties like stability and non-negativity in solutions.
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Please confirm or deny the correctness of my understanding about this definition.

For a given set of ##t_i##s, the matrix ##(B(t_i,t_j))^k_{i,j=1}## is a constant ##k\times k## matrix, whose entries are given by ##B(t_i,t_j)## for each ##i## and ##j##.

The the 'finite' in the last line of the definition refers to ##t_1## and ##t_k## is finite, and ##k## is assumed to be a finite integer.

And if we impose the condition ##t_1<t_2<...<t_k## , then for all finite time slices' ##\{t_i\}_{i=1}^k## means ##\{t_1,...,t_k | (t_1<t_2<...<t_k) \text{ and } (t_i
\in \mathbb{R} \text{ for all } i \in \{1,...,k\}) \text{ and } (-\infty < t_1 < t_k < \infty)\}.##

One such ' time slice' is ##1,2,3,...k##. Another is ##-1,-,\frac{1}{2},...,-\frac{1}{k-1},-\frac{1}{k}.##


A few questions I have are, what information does ##\{t_i\}^k_{i=1}## convey? I find interpreting this notation confusing. Is 'time slice' a precise term at all?

Thank you. 🙂
 
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To add, if someone defines the function ##B## as ##B(s,t)=\mathbb{E}[X_sX_t]##, the matrix ##((B(i,j))_{i,j=1}^k)=M## is symmetric, i.e., ##M_{ij}=M_{ji}=\mathbb{E}[X_sX_t]= \mathbb{E}[X_tX_s].##
 

FAQ: Help understanding the definition of positive semidefinite matrix

What is a positive semidefinite matrix?

A positive semidefinite matrix is a square matrix for which all eigenvalues are non-negative. In other words, a matrix \( A \) is positive semidefinite if for any non-zero vector \( x \), the quadratic form \( x^T A x \) is greater than or equal to zero.

How can you determine if a matrix is positive semidefinite?

To determine if a matrix is positive semidefinite, you can check if all its eigenvalues are non-negative. Alternatively, you can check if all leading principal minors (determinants of the top-left \( k \times k \) submatrices for \( k = 1, 2, \ldots, n \)) are non-negative. Another method is to check if the matrix can be factored as \( A = B^T B \) for some matrix \( B \).

What is the difference between positive definite and positive semidefinite matrices?

A matrix is positive definite if all its eigenvalues are strictly positive and for any non-zero vector \( x \), the quadratic form \( x^T A x \) is strictly greater than zero. In contrast, a positive semidefinite matrix allows for eigenvalues to be zero, and the quadratic form \( x^T A x \) is non-negative but not necessarily strictly positive.

Why are positive semidefinite matrices important?

Positive semidefinite matrices are important in various fields such as optimization, statistics, and machine learning. They often arise in the context of covariance matrices, kernel functions, and quadratic programming problems. Their properties ensure certain desirable characteristics like non-negativity of variance and convexity in optimization problems.

Can a non-symmetric matrix be positive semidefinite?

No, a matrix must be symmetric to be positive semidefinite. The definition of positive semidefiniteness relies on the quadratic form \( x^T A x \), which requires the matrix to be symmetric. Non-symmetric matrices do not satisfy this property and thus cannot be classified as positive semidefinite.

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