I have given an absorbing Markov chain $P_t$ dependent on the transition probabilities $t:V \times V \rightarrow [0,1]$ for $V$ the states of the chain. Given is also a initial vector $x$ and a vector $b$. The goal is then to find $$\min\limits_{t \in [0,1]^{E}} |N x -b|$$ for the fundamental matrix $N_t := (Id-P_t)^{-1}$. To complicate things even further, the transition probabilities are restricted through some functions $(f_i)_{i=1,\ldots, k},~f_i : [0,1]^{E} \rightarrow \mathbb{R}$. The restriction is then of the form $\forall i \in \{1,\ldots,k\}: f_i = 0$. The functions are multivariate rational functions over the transition probabilities and not convex. Given some transition probabilities $t \in [0,1]^E, t = (t_1, \ldots, t_{|E|})$ an example function would be $f(t) = -t_1t_2 + \frac{((-t_3 - t_1t_4)t_5)}{(-1 + t_6t_5 + t_7t_5)}$
As I am an algebraic topologist and do not know much about numerics. What kind of optimization algorithm would one use to solve this problem?