We define $$\|A\|_2=\max_{x\ne0}\frac{\|Ax\|_2}{\|x\|_2}$$ as the matrix norm (Spectral Norm, http://mathworld.wolfram.com/SpectralNorm.html).
If $P,Q$ are definite symmetry matrices and $P+Q=I$. How to prove that $$\|P-Q\|_2\le1$$
My thinking:
In fact, from $P+Q=I$, we can conclude that each pair eigenvalue of P and Q are sum up to 1. As a result, their eigenvalue are all less than 1. But what's the next step to say that $\|P-Q\|_2\le1$?
In general the statement is not true. Take $P = 2I$ and $Q = -I$. Then $P+Q = I$ but $P-Q = 3I$ and $\|3I\|_2 = 3 > 1$
However if you assume that $P$ and $Q$ are both either or negative definite, it could work:
(EDIT it does:) Let $P,Q$ be symmetric, positive definite (spd). Let $v$ be an eigenvector of $Q$. Then
$(P+Q)v = Pv + \lambda v = v \Rightarrow Pv = (1-\lambda)v$
Therefore $v$ is also an eigenvector of $P$ with eigenvalue $(1-\lambda)$. A similar argument shows that every eigenvector of $Q$ is an eigenvector of $P$. Full rank of $P$ and $Q$ implies that every eigenvector of $P-Q$ must be an eigenvector of $P$ and $Q$ also.
Now let $v$ be an eigenvector of both $P - Q$. Then $(P-Q)v = (1-\lambda)v - \lambda v = (1-2\lambda)v$. However both $\lambda$ and $1-\lambda$ are bigger than $0$, since $P$ and $Q$ are spd. That means $\lambda \in [0,1]$ and that implies $(1-2\lambda) \in [-1,1]$. That is true for every eigenvalue of $(P-Q)$, therefore $\|(P-Q)\|_2 \leq 1$.
If $P$ and $Q$ are both negative definite replace $P := -P$ and $Q:= -Q$ and the same argument as above holds.