In this paper, we give a smoothing approximation to the lower order exact penalty functions for inequality-constrained optimization problems. Error estimations are obtained among the optimal objective function values of the smoothed penalty problem, of the nonsmooth penalty problem and of the original optimization problem. An algorithm based on the smoothed penalty function is presented, which is shown to be globally convergent under some mild conditions. Numerical examples are given to illustrate the effectiveness of the present smoothing method.
Zhao, Wenling and Li, Ranran
"A second-order differentiable smoothing approximation lower order exact penalty function,"
Applied Mathematics & Information Sciences: Vol. 06
, Article 14.
Available at: https://dc.naturalspublishing.com/amis/vol06/iss2/14