Convexity of inverse quadratic form Let $\alpha_i\in[0,1]^k$, $x_i\in\mathbb{R}^d$ for all $i\in[k]$, with $k \geq d$. Define $X: \operatorname{col}(X) = \{x_i\}_{i\in[k]}$, $\Lambda(\alpha) = \operatorname{diag}(\alpha)$, $y\in\mathbb{R}^d$.  Is the following expression convex?
$$ f(\alpha) = y^\top\left(\sum_{i=1}^k \alpha_i x_ix_i^\top\right)^{-1}y.$$
Note that the above expression can be expressed equivalently in matrix form as
$$
f(\alpha) =y^T(X\Lambda(\alpha) X^\top)^{-1}y
$$
 A: Claim 1: If $A$ is positive semidefinite then $\langle Ax, x \rangle \langle Ay,y\rangle \geq \langle Ax,y\rangle^{2}$.
This Cauchy--Schwartz inequality can be proved by considering quadratic function $\lambda \mapsto \langle x+\lambda y, A(x+\lambda y) \rangle \geq 0$ and writing down that its discriminant $D = 4 (\langle x, y\rangle ^{2} - \langle x, Ax\rangle \langle y, Ay\rangle$) must be non-positive.
Claim 2: if $A$ is positive definite then $\langle A^{-1}c,c \rangle  =\max_{x}\left\{  2\langle x,c \rangle  - \langle Ax,x\rangle \right\}$ for all vectors $c$.
Indeed, the direction $\langle A^{-1}c,c \rangle \geq 2\langle x,c \rangle  - \langle Ax,x\rangle$ can be obtained as follows:
$\langle A^{-1}c,c \rangle +\langle Ax,x\rangle \geq 2 \sqrt{\langle A^{-1}c,c \rangle \langle Ax,x\rangle} \geq 2|\langle x,c \rangle|$, where the last inequality with $c=Ay$ coincides with Claim 1. Since $A$ is invertible it covers all c's. On the other hans the choice $x=A^{-1}c$ in $\max_{x}\left\{  2\langle x,c \rangle  - \langle Ax,x\rangle \right\}$ gives $\langle A^{-1}c,c \rangle$.
Claim 3: $(\frac{A+B}{2})^{-1} \leq \frac{A^{-1}+B^{-1}}{2}$ holds for all positive definite matrices.
We have $\langle c, (\frac{A+B}{2})^{-1}c \rangle = \max_{x}  \left\{  2\langle x,c \rangle  - \langle (\frac{A+B}{2})x,x\rangle \right\} \leq \frac{\max_{x}  \left\{  2\langle x,c \rangle  - \langle (A)x,x\rangle \right\}}{2} +\frac{\max_{x}  \left\{  2\langle x,c \rangle  - \langle Bx,x\rangle \right\}}{2} = \frac{\langle A^{-1}c,c\rangle + \langle B^{-1}c,c\rangle}{2}$ $\square$.

*

*the idea of representing convex function as a sup of linear functions (or sometimes even sup of concave functions with comparable quantity) is a standard technique in stochastic optimal control theory.

*the link that I provided in the comments, I think the second answer using derivatives  looks correct to me.

A: I have written an answer myself based on the suggestion by @Paata Ivanishvili.  It is based on the following inequality: for any two matrices $\mathbf{X},\mathbf{Y}$, $\lambda\in[0,1]$
$$
(1-\lambda)\mathbf{X}^{-1}+\lambda\mathbf{Y}^{-1} \succeq((1-\lambda)\mathbf{X}+\lambda\mathbf{Y})^{-1}.
$$
If this relation is true, we have
\begin{align}
f(\lambda\alpha +(1-\lambda)\beta) & = y^\top\left(\sum_{i=1}^k (\lambda\alpha_i +(1-\lambda)\beta_i)x_ix_i^\top\right)^{-1}y \\ & \leq \lambda y^\top\left(\sum_{i=1}^k \alpha_i x_ix_i^\top\right)^{-1}y + (1-\lambda) y^\top\left(\sum_{i=1}^k \alpha_i x_ix_i^\top\right)^{-1}y.
\end{align}
