SQP versus SCP methods for nonlinear programming
K. Schittkowski, C. Zillober: in Optimization and Control with
Applications, Qi, Teo and Yang (eds.), Springer, 305-330
We introduce two classes of methods for constrained smooth nonlinear programming, that are widely used in
practice and that are known under the names SQP for sequential quadratic programming and SCP for
sequential convex programming. In both cases convex subproblems are formulated, in the first case a
convex quadratic programming problem, in the second case a convex and separable nonlinear program.
An augmented Lagrangian merit function can be applied for stabilization and for guaranteeing convergence.
The methods are outlined in a uniform way, convergence results are cited, and the results of a
comparative performance evaluation are shown based on a set of 306 standard test problems.
In addition a few industrial applications and case studies are listed that are obtained for the two computer
codes under consideration, i.e. NLPQLP and SCPIP.