Suppose the objective I try to maximize is $$\max_{X} \|(I - \alpha X)^{-1}XA\|_F$$ where $X$ is the matrix needs to be pinned down, $\alpha$ is a scalar, and $\|\cdot\|_F$ is the Frobenius norm. Note that $(I-\alpha X)^{-1}$ is invertible only when $\alpha\neq1$, so assume $\alpha<1$. All matrices are $n$ by $n$ square matrices. An extra condition of matrix $X$ is that the vectors $X(:,k)$ for $k=1,\cdots,n$ is selected from identity by the optimization. So $X(:,k)$ is also a standard basis vector and $X$ may have repeated columns, i.e., $X$ is a permutation matrix.
One possible way to obtain the maxima of the objective is to apply the matrix multiplication via random sampling, where the objective is rewritten as
$$\max_{p_k}\left\| \frac{1}{n}\sum_{k=1}^n\frac{1}{p_k}B(:,k)X(k,:)A\right\|_F$$
where $B$ is the inverse and be approximated by Neumann series such that $$B = (I - \alpha X)^{-1} = \sum_{i=0}^n (\alpha X)^k$$
Note that we do not know the form or the specification of $X$ in advance but choose each column of $X$ by random sampling with probability $p_k$.
Now I have 3 questions:
(1) How to compute $B$ when it is random sampling approximation?
(2) Is this method too complex to implement on numerical calculation?
(3) Is there any better way to solve this puzzle?
Thanks in advance!
