Outer products & positive (semi-) definiteness

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In summary, the conversation discusses the properties of a symmetric matrix ##M## defined as the sum of two outer products of two vectors in the same Euclidean space. The question is whether or not ##M## is positive (semi)definite, and the equivalent conditions are stated as ##(x\cdot u)(x\cdot v)\geq 0## for all vectors ##x##. The properties of ##M## can be reduced to those of a 2x2 Hermitian form ##Q##, which can be easily determined by computing its eigenvalues.
  • #1
economicsnerd
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Let ##u,v## be vectors in the same Euclidean space, and define the symmetric matrix ##M = uv'+vu'##, the sum of their two outer products.

I'm interested in whether or not ##M## is positive (semi)definite.

Does anybody know of any equivalent conditions that I might phrase "directly" in terms of the vectors ##u,v##?
 
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  • #2
An equivalent condition is ##(x\cdot u)(x\cdot v)\geq 0## for all vectors ##x##.
 
  • #3
Let ##z## be an arbitrary vector, then we are interested in the properties of
$$z^\dagger M z = \langle z, u \rangle \langle v, z \rangle + \langle z, v \rangle \langle u, z \rangle .$$
Assume that ##v\neq u##, then we can write
$$ z = a u + b v + z_\perp,~~~\langle z_\perp, u \rangle = \langle z_\perp, v \rangle =0.$$
Then, with ## A = (a~~b)^T##, we have
$$z^\dagger M z = A^\dagger Q A,$$
where ##Q## is a Hermitian 2x2 form determined in terms of the inner products of ##u## and ##v##. Positivity properties of ##M## are reduced to those of ##Q## for which it is simple to compute the eigenvalues.
 

1. What is an outer product?

An outer product is a mathematical operation that takes two vectors and produces a matrix. It is also known as the tensor product or Kronecker product. The resulting matrix has the same number of rows as the first vector and the same number of columns as the second vector.

2. How is an outer product related to positive definite matrices?

An outer product can be used to construct positive definite matrices. By taking the outer product of a vector with itself, you can create a positive definite matrix. This is because the resulting matrix will have only positive eigenvalues, which is a characteristic of positive definite matrices.

3. What is the difference between positive definite and positive semi-definite matrices?

A positive definite matrix has all positive eigenvalues, meaning that all of its eigenvalues are greater than zero. A positive semi-definite matrix has at least one eigenvalue that is equal to zero, but the rest are positive. In other words, a positive semi-definite matrix can have zero eigenvalues, while a positive definite matrix cannot.

4. How is positive (semi-) definiteness related to the concept of positive (semi-) definiteness of a quadratic form?

The positive (semi-) definiteness of a quadratic form is determined by the eigenvalues of the associated matrix. If all of the eigenvalues are positive, the quadratic form is positive definite. If at least one eigenvalue is zero, the quadratic form is positive semi-definite. This relationship is important because it allows us to determine the definiteness of a quadratic form by analyzing the associated matrix.

5. Can a matrix be both positive definite and positive semi-definite?

No, a matrix cannot be both positive definite and positive semi-definite. This is because a positive definite matrix must have all positive eigenvalues, while a positive semi-definite matrix can have at least one eigenvalue equal to zero. Therefore, a positive definite matrix cannot have any zero eigenvalues, which is a requirement for a matrix to be positive semi-definite.

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