A Modified Feasible SSLE Algorithm for Nonlinearly Constrained Optimization

Abstract

Sequential systems of linear equations methods(SSLE) are proposed mainly to solve the inconsistency and computation problems existed in the classical SQP method. In this paper, a new algorithm for inequality constrained optimization problems is presented. The attractive feature of the new algorithm is that only one system of linear equations is required to obtain the revised feasible descent direction. In order to void the Maratos effect, combined the generalized projection technique, a height-order correction direction is computed by an explicit formula, and it plays a important role in avoiding the strict complementarity. Furthermore, its global and superlinear convergence rate are obtained under some suitable conditions.