in python please

- Write a program to roughly carry out the optimization problem inherent in A, namely maxx=OnxAx. The program should take an arbitrary mn matrix A and sample over 100000 random vectors xRn (and use Gaussian random numbers) to approximately pick the maximum. Compute and empirically show that the analytic solution is an upper bound for the search. Argue if you have really shown anything and/or if the empirical search is merely suggestive (that is, it is not a proof of anything). For demonstration purposes, use m=9 and n=6. The matrix entries can also be randomly chosen