A local search method for optimization problem with d.c. inequality constraints

This paper addresses a nonconvex optimization problem with the cost function and inequality constraints given by d.c. functions. The original problem is reduced to a problem without inequality constraints by the exact penalization procedure. A special local search method for the penalized problem is developed, which is based, first, on the linearization procedure with respect to the basic nonconvexity and, second, on the consecutive solutions of linearized convex problems. Convergence properties of the method are investigated. In particular, it is shown that a limit point of the sequence produced by the method is considerably stronger than the usual KKT-vector. In addition, the relations between an approximate solution of linearized convex problem and the KKT-vector of the original problem are established, and the various stopping criteria are substantiated. Besides, we established the relations among the Lagrange multipliers of the original problem, those ones of the linearized problem, and the value of the penalty parameter. Finally, a preliminary computational testing of the LSM developed has been carried out on several test problems taken from literature.