The goal of the article is to study Shannon function estimates for the length of the minimal implicating vector. We obtain upper and lower bounds on the Shannon function that differ only slightly. These results may be used for creating controlled generators of random codes, in the theory of probabilistic automata and the theory of convex polyhedra.

minimal implicating vector, Shannon function, gradient algorithm, integer linear programming, stochastic matrices

Mathematical problems and theory of numerical methods