An algorithm for a class of functional inequality constrained optimization problems

In this paper, we develop a new computational method for solving a class of functional inequality constrained optimization problems. The main idea of this method is that the inequality constraint is transformed into a equality constraint which is smoothed via a twice continuously differentiable function, and treated as a penalty function. On this basis, the smoothed problem can be solved using any second-order gradient algorithm. Finally, the validity of the proposed method is shown by four numerical experiments.