MHB Positive Definite Matrices and Their Properties

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The discussion focuses on proving that both XX^T and X^TX are positive semidefinite for a matrix X in R^(d×n). It establishes that if X has rank d, then XX^T is positive definite and invertible. The proof involves showing that for any vector x, the expressions x^T(XX^T)x and (Xx)^T(Xx) yield non-negative results, confirming the positive semidefiniteness. The conversation also clarifies the distinction between the dimensions of x when applying these properties to X^TX. Overall, the participants engage in a mathematical exploration of matrix properties and seek guidance on specific proof steps.
Fernando Revilla
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I quote a question from Yahoo! Answers

1. Prove that if X ∈ R^(d×n) then XX^T and X^TX are both positive semidefinite.
6. Prove that if X ∈ R^(d×n) has rank d, then XX^T is positive definite (invertible).

I have given a link to the topic there so the OP can see my response.
 
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For all $x\in\mathbb{R}^{d\times 1}:$
$$x^T(XX^T)x=(X^Tx)^T(X^Tx)=(y_1,\ldots,y_n) \begin{pmatrix}{y_1}\\{\vdots}\\{y_n}\end{pmatrix}=y_1^2+\ldots+y_n^2\geq 0$$
which implies $XX^T$ is positive semidefinite (or positive definite). Similar arguments for $X^TX$.

If $\text{rank }X=d$, then $\text{rank }(XX^T)=\text{rank }X=d$, which implies $XX^T$ is invertible. This means that $XX^T$ is congruent to a matrix $\text{diag }(\alpha_1,\ldots,\alpha_d)$ with $\alpha_i>0$ for all $i$, as a consequence $XX^T$ is positive definite
 
When I try to solve the case for XTX I get stuck at the following:
xT(XTX)x = xTXTXx = (Xx)TXx

Please kindly guide me next step.
 
MrJava said:
When I try to solve the case for XTX I get stuck at the following: xT(XTX)x = xTXTXx = (Xx)TXx

Right. Now, the difference is that $x\in \mathbb{R}^{n\times 1}$ instead of $\mathbb{R}^{d\times 1}.$ So, for all $x\in \mathbb{R}^{n\times 1}$
$$(Xx)^T(Xx)=(w_1,\ldots,w_d) \begin{pmatrix}{w_1}\\{\vdots}\\{w_d}\end{pmatrix} =w_1^2+\ldots+w_d^2\geq 0$$
 
Fernando Revilla said:
Right. Now, the difference is that $x\in \mathbb{R}^{n\times 1}$ instead of $\mathbb{R}^{d\times 1}.$ So, for all $x\in \mathbb{R}^{n\times 1}$
$$(Xx)^T(Xx)=(w_1,\ldots,w_d) \begin{pmatrix}{w_1}\\{\vdots}\\{w_d}\end{pmatrix} =w_1^2+\ldots+w_d^2\geq 0$$

Ok I get the point, thank you.
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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