A SQP method for minimization of locally Lipschitz functions with nonlinear constraints


In this paper, we present a quadratic model for minimizing problems with nonconvex and nonsmooth objective and constraint functions. This method is based on sequential quadratic programming
that uses an l1 penalty function to equilibrate among the decrease
of the objective function and the feasibility of the constraints. To
construct a quadratic subproblem, we linearize the objective and constraint functions with their