Global probability maximization for a Gaussian bilateral inequality in polynomial time

摘要: The present paper investigates Gaussian bilateral inequalities in view of solving related probability maximization problems. Since the function f representing satisfaction a given inequality is not concave everywhere, we first state and prove necessary sufficient condition for negative semi-definiteness Hessian. Then, (nonconvex) problem globally maximizing over polyhedron $$\mathbb {R}^{n}$$ adressed, shown to be polynomial-time solvable, thus yielding new-comer (short) list nonconvex global optimization problems which can solved exactly polynomial time. Application computing upper bounds maximum joint set m independent discussed computational results are reported.