Positive Definiteness Determined from Symmetrized Products

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In summary, "positive definiteness determined from symmetrized products" is a mathematical concept used to determine if a symmetric matrix is positive definite. It takes into account the symmetry of the matrix and considers all possible combinations of elements to determine the positivity of the resulting values. This method differs from others by providing more accurate results, being applicable to matrices of any size, and having various real-world applications. However, it may have limitations in terms of computational cost and applicability to matrices with complex or imaginary elements. Overall, it is a useful tool in scientific research for analyzing positive definiteness and its applications in optimization, physics, economics, and data analysis.
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Suppose ##P## and ##Q## are self-adjoint linear operators on a finite dimensional, complex inner product space. Assume both ##P## and the symmetrized product ##PQ + QP## are positive definite. Show that ##Q## must also be positive definite.
 
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Note that as [itex]P[/itex] and [itex]Q[/itex] are self-adoint on a finite-dimensional complex inner product space [itex](V, \langle \cdot, \cdot \rangle)[/itex], their eigenvalues are real and they each have a basis of orthogonal eigenvectors.

By definition, a linear map [itex]L: V \to V[/itex] is positive definite if and only if [itex]\langle Lx ,x \rangle \geq 0[/itex] for every [itex]x \in V[/itex]. If in addition [itex]L[/itex] is self-adjoint, it is positive definite if all of its eigenvalues are non-negative, since we can take an orthonormal basis of eigenvectors [itex]x_i[/itex] with eigenvalues [itex]\lambda_i[/itex] so that for arbitrary [itex]v = \sum_i v_i x_i[/itex], [tex]
\langle Lv ,v \rangle = \sum_i \sum_j v_i v_j^{*} \lambda_i \langle x_i, x_j \rangle = \sum_i |v_i|^2 \lambda_i.[/tex]

Let [itex]x[/itex] be an eigenvector of [itex]Q[/itex] with eigenvalue [itex]\lambda[/itex]. Then by positive-definitenes of [itex]PQ + QP[/itex], [tex]
\begin{split}
0 &\leq \langle (PQ + QP)x, x \rangle \\
&= \langle PQ x, x \rangle + \langle QPx, x \rangle \\
&= \lambda \langle Px, x \rangle + \langle Px, Qx \rangle \qquad \mbox{(by self-adjointness of $Q$)} \\
&= \lambda \langle Px, x \rangle + \langle Px, \lambda x \rangle \\
&= \lambda \langle Px, x \rangle + \lambda \langle Px, x \rangle \qquad \mbox{(since $\lambda \in \mathbb{R}$)} \\
&= 2\lambda \langle Px, x \rangle. \end{split}[/tex] Now [itex]\langle Px ,x \rangle \geq 0[/itex] by positive definiteness of [itex]P[/itex], so we must have [itex]\lambda \geq 0[/itex] and [itex]Q[/itex] is positive-definite.
 
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1. What is positive definiteness determined from symmetrized products?

Positive definiteness determined from symmetrized products is a mathematical concept used to determine whether a matrix is positive definite. It involves taking the symmetrized product of the matrix with itself and checking if all the eigenvalues are positive.

2. Why is positive definiteness important?

Positive definiteness is important because it ensures that a matrix is well-behaved and has certain desirable properties, such as having a unique inverse and being a minimum or maximum of a quadratic form. It is also a key requirement in many mathematical and statistical applications.

3. How is positive definiteness determined from symmetrized products used in real-world applications?

Positive definiteness determined from symmetrized products is used in a variety of real-world applications, including optimization problems, signal processing, and machine learning. It is also used in physics, particularly in quantum mechanics, to determine the stability of a system.

4. Can a matrix be positive definite if it is not symmetric?

No, a matrix cannot be positive definite if it is not symmetric. Positive definiteness determined from symmetrized products relies on the symmetry of the matrix to ensure that all eigenvalues are positive. If a matrix is not symmetric, this method cannot be used to determine positive definiteness.

5. Are there alternative methods for determining positive definiteness?

Yes, there are alternative methods for determining positive definiteness, such as checking if all principal minors of the matrix are positive or using the Cholesky decomposition. However, positive definiteness determined from symmetrized products is a commonly used and efficient method, especially for larger matrices.

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