The cone of symmetric positive semidefinite $n\times n$ matrices is the convex hull of rank $1$ matrices. That is, every symmetric positive semidefinite matrix is a convex combination of rank 1 matrices.



> 
> Does this property generalize to solutions of linear systems of semidefinite matrices? 
> 
> 
> 


Let me be precise. Fix $k$ symmetric $n\times n$ matrices $A\_1,\ldots, A\_k$. Consider the system of linear equations $\langle A\_1,X\rangle=\cdots = \langle A\_k, X\rangle = 0$, which you want to solve for a symmetric semidefinite $n\times n$ matrix $X\succeq 0$. Here, the inner product of two matrices is $\langle(a\_{ij}),(b\_{ij})\rangle=\sum\_{i,j = 1}^n a\_{ij}b\_{ij}$.


The set of solutions $X$ forms a closed convex subcone $C$ of the cone of semidefinite $n\times n$ matrices. Is $C$ the convex hull of its rank 1 matrices? Namely, is every solution a convex combination of rank 1 solutions?



Is a solution of a linear system of semidefinite matrices a convex combination of rank 1 solutions?
No. Try $n=k=2$ with $A\_1 = \pmatrix{1 & 0\cr 0 & -1\cr}$ and $A\_2 = \pmatrix{1 & 1\cr 1 & -1\cr}$. The only symmetric matrices $X$ with $(A\_1,X) = (A\_2,X) = 0$ are multiples of $I$, so there are no rank 1 solutions.


