Probabilistic analysis of an approximation algorithm for the $m$-peripatetic salesman problem on random instances unbounded from above

We present the probabilistic analysis of an approximation algorithm for the minimum-weight $m$-peripatetic salesman problem with different weight functions of their routes (Hamiltonian cycles). The time complexity of the algorithm is $O(mn^2)$. We assume that the entries of the distance matrix are independent equally distributed random variables with values in an upper-unbounded domain $[a_n,\infty)$, where $a_n>0$. The analysis is carried out for the example of truncated normal and exponential distributions. Estimates for the relative error and failure probability, as well as conditions for the asymptotic exactness of the algorithm, are found.