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I have a question: If $A$ and $B$ are $n\times n$ positive semidefinite matrices, why does it follow that $(A+B)^{2}$ is positive semidefinite?

I know that to prove that $(A+B)^{2}$ is positive semidefinite, I need to prove that the eigenvalues of $(A+B)^{2}$ are nonnegative, but I don't know how to about this.

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    It would be useful for you to specify whether the definition of *positive semidefinite* that you are using implicitly implies the matrices are symmetric or not. See my answer below for more details.2012-04-22

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The answer is: It depends!

There are two common definitions of positive semidefiniteness.

Definition 1: $\newcommand{\m}{\mathbf}\newcommand{\A}{\m A}\newcommand{\B}{\m B}\newcommand{\x}{\m x} \A \in M_n(\mathbb R)$ is positive semidefinite if and only if for all $\x \in \mathbb R^n$ we have $\x^T \A \x \geq 0$.

Definition 2: Same as Definition 1, with the additional requirement that $\A$ be symmetric.

Let's start with the second definition, which may be the more commonly assumed. In this case, the stated result is true and follows easily from the definitions. In particular, $$ \x^T(\A + \B)\x = \x^T \A \x + \x^T \B \x \geq 0 \>, $$ since both terms in the middle are nonnegative. Furthermore if $\A$ and $\B$ are both symmetric, then so is $\A + \B$. Hence, $$ \x^T (\A + \B)^2 \x = \x^T (\A+\B)^T (\A+\B) \x = \|(\A+\B) \x\|_2^2 \geq 0 \>. $$

Now, let's look at the first definition. If we drop the assumption that $\A$ and $\B$ are symmetric, then the stated result in the question is false.

It is, of course, still true that $\A+\B$ is positive semidefinite (the same proof above applies), but $(\A+\B)^2$ may not be.

Counterexample: It suffices to consider only a single matrix $\A$, with $\B = 0$. Take $$\A = \left(\begin{array}{rr} 1 & -2 \\ 0 & 1\end{array}\right) \>.$$ Then, if $\x = (x_i)$, $$ \x^T \A \x = x_1^2 - 2 x_1 x_2 + x_2^2 = (x_1 - x_2)^2 \geq 0 \>. $$ So, $\A$ is positive semidefinite. However, $$ \A^2 = \left(\begin{array}{rr} 1 & -4 \\ 0 & 1\end{array}\right) \>, $$ which is clearly not positive semidefinite; to see this, just take $\x$ to be a vector of ones.

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First prove that the sum of positive semidefinite is positive semidefinite, and then prove that the square of positive semidefinite is positive semidefinite.

Added in edit: here's the proof.

Recall that a matrix $A$ is positive semidefinite if $z^TAz\geq0$ for all $z\in\mathbb{R}^n$. So, if $A,B$ are positive semidefinite and $z\in\mathbb{R}^n$, then $$ z^T(A+B)z=z^TAz+z^TBz\geq0, $$ so $A+B$ is positive semidefinite.

And for the square of positive semidefinite, it is enough to show that the square of a symmetric matrix is positive semidefinite. Indeed, $$ z^TA^2z=z^TA^TAz=(Az)^T(Az)=\|Az\|^2\geq0. $$

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    it is easy to prove that the square of positive semidefinite matrix is positive semidefinite. But, I don't know how to prove that the sum of two positive semidefinite matrices is positive senidefinite. I know that $tr(A+B)=tr(A)+tr(B)\geq 0$, but this doesn't imply that the eigenvalues of $(A+B)$ are all nonnegative. Can you, please, prove it?2012-04-21
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    I've edited the answer to include the proof.2012-04-21
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    This answer seems to assume that $A$ and $B$ are symmetric.2012-04-22
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    @cardinal: yes indeed. I fail to see what's the point of considering positive semidefiniteness for non-symmetric matrices. You can have the definition, but what properties can you obtain from it?2012-04-22
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    I agree that the theory is much richer once symmetry is imposed. But, the alternate definition not assuming symmetry is, of course, not mine. Indeed, I was thrown off myself when reading your answer at first, given the definition you provide! :)2012-04-22