The Dual Algorithm Based on a Class of Nonlinear Lagrangians for Nonlinear Programming

This paper studies the properties of a class of nonlinear Lagrangians for nonlinear programming with inequality constraints. It's shown that under a set of conditions this class of Lagrange algorithm is locally convergent when the penalty parameter is larger than a threshold. An error bound estimate of the solution, depending on the penalty, is also established. The paper also discusses the properties of the dual function associated with the proposed nonlinear Lagrangians. Finally, the dual algorithm corresponding to the proposed nonlinear Lagrangians is developed and used to solve some numerical examples by using the nonlinear Lagrangians in the literature. Numerical results suggest that the dual algorithm is effective for solving nonlinear programming.