A Successive Quadratic Programming Decomposition Method for Large-Scale Nonlinear Programming Problems

Abstract

Large-scale nonlinear programming problems usually contain a relatively small number of nonlinear variables. For such a problem, it is often effective to treat the linear part as a linear programming problem by temporarily fixing the nonlinear variables. As a result, we obtain a nonsmooth optimization problem containing the nonlinear variables only. Based on this idea, we propose a successive quadratic programming algorithm for large-scale nonlinear programming problems. We show convergence of the algorithm and report some computational experience.