In this article, we discuss a nondifferentiable nonlinear penalty method for an optimization problem with inequality constraints. A smoothing method is proposed for the nonsmooth nonlinear penalty function. Error estimations are obtained among the optimal value of smoothed penalty problem, the optimal value of the nonsmooth nonlinear penalty optimization problem and that of the original constrained optimization problem. We give an algorithm for the constrained optimization problem based on the smoothed nonlinear penalty method and prove the convergence of the algorithm. The efficiency of the smoothed nonlinear penalty method is illustrated with a numerical example.Department of Applied Mathematic
For constrained nonsmooth optimization problems, continuously differentiable penalty functions and b...
We introduce the concept of partially strictly monotone functions and apply it to construct a class ...
In this article, we study the nonlinear penalization of a constrained optimization problem and show ...
In this study, a new smoothing nonlinear penalty function for constrained optimization problems is p...
Abstract For inequality constrained optimization problem, we first propose a new smoothing method to...
In this paper, we give a smoothing approximation to the lower order exact penalty functions for ineq...
Abstract In this paper, we propose a method to smooth the general lower-order exact penalty function...
Abstract. In this paper we consider inequality constrained nonlinear optimization problems where the...
For inequality constrained minimization problem, we first propose a new exact nonsmooth objective pe...
AbstractThe paper introduces a smoothing technique for a lower order penalty function for constraine...
In this paper we consider inequality constrained nonlinear optimization problems where the first ord...
AbstractIn this paper, we propose a novel objective penalty function for inequality constrained opti...
This paper considers a class of constrained optimization problems with a possi-bly nonconvex non-Lip...
In this paper, we propose new linesearch-based methods for nonsmooth constrained optimization proble...
We introduce the concept of partially strictly monotone functions and apply it to construct a class ...
For constrained nonsmooth optimization problems, continuously differentiable penalty functions and b...
We introduce the concept of partially strictly monotone functions and apply it to construct a class ...
In this article, we study the nonlinear penalization of a constrained optimization problem and show ...
In this study, a new smoothing nonlinear penalty function for constrained optimization problems is p...
Abstract For inequality constrained optimization problem, we first propose a new smoothing method to...
In this paper, we give a smoothing approximation to the lower order exact penalty functions for ineq...
Abstract In this paper, we propose a method to smooth the general lower-order exact penalty function...
Abstract. In this paper we consider inequality constrained nonlinear optimization problems where the...
For inequality constrained minimization problem, we first propose a new exact nonsmooth objective pe...
AbstractThe paper introduces a smoothing technique for a lower order penalty function for constraine...
In this paper we consider inequality constrained nonlinear optimization problems where the first ord...
AbstractIn this paper, we propose a novel objective penalty function for inequality constrained opti...
This paper considers a class of constrained optimization problems with a possi-bly nonconvex non-Lip...
In this paper, we propose new linesearch-based methods for nonsmooth constrained optimization proble...
We introduce the concept of partially strictly monotone functions and apply it to construct a class ...
For constrained nonsmooth optimization problems, continuously differentiable penalty functions and b...
We introduce the concept of partially strictly monotone functions and apply it to construct a class ...
In this article, we study the nonlinear penalization of a constrained optimization problem and show ...